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       "(200, 504)"
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     "execution_count": 11,
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     "output_type": "execute_result"
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   ],
   "source": [
    "#!/usr/bin/python3\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "stock_day_change=np.load('./stock_day_change.npy')\n",
    "stock_day_change.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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  {
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       "      <td>-0.416191</td>\n",
       "      <td>-0.951516</td>\n",
       "      <td>-0.942907</td>\n",
       "      <td>1.365243</td>\n",
       "      <td>-0.030679</td>\n",
       "      <td>0.466483</td>\n",
       "      <td>0.842319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票1</th>\n",
       "      <td>-0.204016</td>\n",
       "      <td>-0.469136</td>\n",
       "      <td>-0.493505</td>\n",
       "      <td>-1.031958</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.251092</td>\n",
       "      <td>-1.664318</td>\n",
       "      <td>-0.028633</td>\n",
       "      <td>0.741661</td>\n",
       "      <td>-0.906842</td>\n",
       "      <td>...</td>\n",
       "      <td>0.386705</td>\n",
       "      <td>0.139215</td>\n",
       "      <td>0.851137</td>\n",
       "      <td>1.197187</td>\n",
       "      <td>-0.580815</td>\n",
       "      <td>0.923829</td>\n",
       "      <td>0.807600</td>\n",
       "      <td>-1.890525</td>\n",
       "      <td>0.448080</td>\n",
       "      <td>0.406878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 504 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6    \\\n",
       "股票0 -1.043429 -0.021399  0.020054  1.000000 -1.084019 -0.983107 -0.380247   \n",
       "股票1 -0.204016 -0.469136 -0.493505 -1.031958  1.000000  0.251092 -1.664318   \n",
       "\n",
       "          7         8         9    ...       494       495       496  \\\n",
       "股票0  1.794049 -0.832640  0.449565  ... -1.459074 -0.129370 -1.117201   \n",
       "股票1 -0.028633  0.741661 -0.906842  ...  0.386705  0.139215  0.851137   \n",
       "\n",
       "          497       498       499       500       501       502       503  \n",
       "股票0 -0.416191 -0.951516 -0.942907  1.365243 -0.030679  0.466483  0.842319  \n",
       "股票1  1.197187 -0.580815  0.923829  0.807600 -1.890525  0.448080  0.406878  \n",
       "\n",
       "[2 rows x 504 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 股票 0-> 股票 stock_day_change.shape(0)\n",
    "stock_symbols=['股票'+str(x) for  x in range(stock_day_change.shape[0])]\n",
    "#print(stock_symbols)\n",
    "pd.DataFrame(stock_day_change,index=stock_symbols).head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7dfbd5ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2018-01-01</th>\n",
       "      <th>2018-01-02</th>\n",
       "      <th>2018-01-03</th>\n",
       "      <th>2018-01-04</th>\n",
       "      <th>2018-01-05</th>\n",
       "      <th>2018-01-06</th>\n",
       "      <th>2018-01-07</th>\n",
       "      <th>2018-01-08</th>\n",
       "      <th>2018-01-09</th>\n",
       "      <th>2018-01-10</th>\n",
       "      <th>...</th>\n",
       "      <th>2019-05-10</th>\n",
       "      <th>2019-05-11</th>\n",
       "      <th>2019-05-12</th>\n",
       "      <th>2019-05-13</th>\n",
       "      <th>2019-05-14</th>\n",
       "      <th>2019-05-15</th>\n",
       "      <th>2019-05-16</th>\n",
       "      <th>2019-05-17</th>\n",
       "      <th>2019-05-18</th>\n",
       "      <th>2019-05-19</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>股票0</th>\n",
       "      <td>-1.043429</td>\n",
       "      <td>-0.021399</td>\n",
       "      <td>0.020054</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.084019</td>\n",
       "      <td>-0.983107</td>\n",
       "      <td>-0.380247</td>\n",
       "      <td>1.794049</td>\n",
       "      <td>-0.832640</td>\n",
       "      <td>0.449565</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.459074</td>\n",
       "      <td>-0.129370</td>\n",
       "      <td>-1.117201</td>\n",
       "      <td>-0.416191</td>\n",
       "      <td>-0.951516</td>\n",
       "      <td>-0.942907</td>\n",
       "      <td>1.365243</td>\n",
       "      <td>-0.030679</td>\n",
       "      <td>0.466483</td>\n",
       "      <td>0.842319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票1</th>\n",
       "      <td>-0.204016</td>\n",
       "      <td>-0.469136</td>\n",
       "      <td>-0.493505</td>\n",
       "      <td>-1.031958</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.251092</td>\n",
       "      <td>-1.664318</td>\n",
       "      <td>-0.028633</td>\n",
       "      <td>0.741661</td>\n",
       "      <td>-0.906842</td>\n",
       "      <td>...</td>\n",
       "      <td>0.386705</td>\n",
       "      <td>0.139215</td>\n",
       "      <td>0.851137</td>\n",
       "      <td>1.197187</td>\n",
       "      <td>-0.580815</td>\n",
       "      <td>0.923829</td>\n",
       "      <td>0.807600</td>\n",
       "      <td>-1.890525</td>\n",
       "      <td>0.448080</td>\n",
       "      <td>0.406878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票2</th>\n",
       "      <td>2.225527</td>\n",
       "      <td>-0.538589</td>\n",
       "      <td>-0.034119</td>\n",
       "      <td>1.835529</td>\n",
       "      <td>0.534756</td>\n",
       "      <td>0.711145</td>\n",
       "      <td>-1.418851</td>\n",
       "      <td>-1.275283</td>\n",
       "      <td>-0.273687</td>\n",
       "      <td>-0.016900</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.701764</td>\n",
       "      <td>0.226368</td>\n",
       "      <td>0.175136</td>\n",
       "      <td>-1.460275</td>\n",
       "      <td>-0.234753</td>\n",
       "      <td>0.563395</td>\n",
       "      <td>1.227940</td>\n",
       "      <td>-0.114374</td>\n",
       "      <td>-0.303904</td>\n",
       "      <td>-1.390678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票3</th>\n",
       "      <td>1.227869</td>\n",
       "      <td>0.636877</td>\n",
       "      <td>1.120585</td>\n",
       "      <td>-0.291920</td>\n",
       "      <td>-0.487572</td>\n",
       "      <td>-0.326122</td>\n",
       "      <td>-0.364033</td>\n",
       "      <td>-1.015324</td>\n",
       "      <td>0.064857</td>\n",
       "      <td>0.891030</td>\n",
       "      <td>...</td>\n",
       "      <td>0.369404</td>\n",
       "      <td>-0.309433</td>\n",
       "      <td>-0.010762</td>\n",
       "      <td>-2.573608</td>\n",
       "      <td>0.810395</td>\n",
       "      <td>0.324651</td>\n",
       "      <td>0.646900</td>\n",
       "      <td>0.960642</td>\n",
       "      <td>0.845781</td>\n",
       "      <td>-0.215565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票4</th>\n",
       "      <td>0.423927</td>\n",
       "      <td>1.071454</td>\n",
       "      <td>0.609274</td>\n",
       "      <td>0.013996</td>\n",
       "      <td>3.567805</td>\n",
       "      <td>-0.623823</td>\n",
       "      <td>1.780912</td>\n",
       "      <td>-0.352848</td>\n",
       "      <td>-0.185834</td>\n",
       "      <td>-0.459300</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.829237</td>\n",
       "      <td>0.830750</td>\n",
       "      <td>0.498569</td>\n",
       "      <td>0.822506</td>\n",
       "      <td>0.108058</td>\n",
       "      <td>0.441951</td>\n",
       "      <td>-0.104546</td>\n",
       "      <td>-0.566049</td>\n",
       "      <td>0.607293</td>\n",
       "      <td>1.191645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 504 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     2018-01-01  2018-01-02  2018-01-03  2018-01-04  2018-01-05  2018-01-06  \\\n",
       "股票0   -1.043429   -0.021399    0.020054    1.000000   -1.084019   -0.983107   \n",
       "股票1   -0.204016   -0.469136   -0.493505   -1.031958    1.000000    0.251092   \n",
       "股票2    2.225527   -0.538589   -0.034119    1.835529    0.534756    0.711145   \n",
       "股票3    1.227869    0.636877    1.120585   -0.291920   -0.487572   -0.326122   \n",
       "股票4    0.423927    1.071454    0.609274    0.013996    3.567805   -0.623823   \n",
       "\n",
       "     2018-01-07  2018-01-08  2018-01-09  2018-01-10  ...  2019-05-10  \\\n",
       "股票0   -0.380247    1.794049   -0.832640    0.449565  ...   -1.459074   \n",
       "股票1   -1.664318   -0.028633    0.741661   -0.906842  ...    0.386705   \n",
       "股票2   -1.418851   -1.275283   -0.273687   -0.016900  ...   -0.701764   \n",
       "股票3   -0.364033   -1.015324    0.064857    0.891030  ...    0.369404   \n",
       "股票4    1.780912   -0.352848   -0.185834   -0.459300  ...   -0.829237   \n",
       "\n",
       "     2019-05-11  2019-05-12  2019-05-13  2019-05-14  2019-05-15  2019-05-16  \\\n",
       "股票0   -0.129370   -1.117201   -0.416191   -0.951516   -0.942907    1.365243   \n",
       "股票1    0.139215    0.851137    1.197187   -0.580815    0.923829    0.807600   \n",
       "股票2    0.226368    0.175136   -1.460275   -0.234753    0.563395    1.227940   \n",
       "股票3   -0.309433   -0.010762   -2.573608    0.810395    0.324651    0.646900   \n",
       "股票4    0.830750    0.498569    0.822506    0.108058    0.441951   -0.104546   \n",
       "\n",
       "     2019-05-17  2019-05-18  2019-05-19  \n",
       "股票0   -0.030679    0.466483    0.842319  \n",
       "股票1   -1.890525    0.448080    0.406878  \n",
       "股票2   -0.114374   -0.303904   -1.390678  \n",
       "股票3    0.960642    0.845781   -0.215565  \n",
       "股票4   -0.566049    0.607293    1.191645  \n",
       "\n",
       "[5 rows x 504 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days=pd.date_range('2018-1-1', periods=stock_day_change.shape[1], freq='1d')\n",
    "df=pd.DataFrame(stock_day_change,index=stock_symbols, columns=days)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cfc770d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>股票0</th>\n",
       "      <th>股票1</th>\n",
       "      <th>股票2</th>\n",
       "      <th>股票3</th>\n",
       "      <th>股票4</th>\n",
       "      <th>股票5</th>\n",
       "      <th>股票6</th>\n",
       "      <th>股票7</th>\n",
       "      <th>股票8</th>\n",
       "      <th>股票9</th>\n",
       "      <th>...</th>\n",
       "      <th>股票190</th>\n",
       "      <th>股票191</th>\n",
       "      <th>股票192</th>\n",
       "      <th>股票193</th>\n",
       "      <th>股票194</th>\n",
       "      <th>股票195</th>\n",
       "      <th>股票196</th>\n",
       "      <th>股票197</th>\n",
       "      <th>股票198</th>\n",
       "      <th>股票199</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>-1.043429</td>\n",
       "      <td>-0.204016</td>\n",
       "      <td>2.225527</td>\n",
       "      <td>1.227869</td>\n",
       "      <td>0.423927</td>\n",
       "      <td>0.020898</td>\n",
       "      <td>1.231141</td>\n",
       "      <td>1.291075</td>\n",
       "      <td>-2.053475</td>\n",
       "      <td>-0.331716</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.469176</td>\n",
       "      <td>-1.172991</td>\n",
       "      <td>-1.363139</td>\n",
       "      <td>-1.203259</td>\n",
       "      <td>0.538908</td>\n",
       "      <td>-1.719053</td>\n",
       "      <td>0.366640</td>\n",
       "      <td>0.881434</td>\n",
       "      <td>0.762832</td>\n",
       "      <td>0.156397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>-0.021399</td>\n",
       "      <td>-0.469136</td>\n",
       "      <td>-0.538589</td>\n",
       "      <td>0.636877</td>\n",
       "      <td>1.071454</td>\n",
       "      <td>0.922378</td>\n",
       "      <td>-1.917052</td>\n",
       "      <td>0.306130</td>\n",
       "      <td>-0.498284</td>\n",
       "      <td>-0.069999</td>\n",
       "      <td>...</td>\n",
       "      <td>0.067329</td>\n",
       "      <td>1.704009</td>\n",
       "      <td>-0.516708</td>\n",
       "      <td>-0.189825</td>\n",
       "      <td>0.376453</td>\n",
       "      <td>0.099424</td>\n",
       "      <td>-0.439883</td>\n",
       "      <td>0.139151</td>\n",
       "      <td>0.418381</td>\n",
       "      <td>-0.936374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 200 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 股票0       股票1       股票2       股票3       股票4       股票5  \\\n",
       "2018-01-01 -1.043429 -0.204016  2.225527  1.227869  0.423927  0.020898   \n",
       "2018-01-02 -0.021399 -0.469136 -0.538589  0.636877  1.071454  0.922378   \n",
       "\n",
       "                 股票6       股票7       股票8       股票9  ...     股票190     股票191  \\\n",
       "2018-01-01  1.231141  1.291075 -2.053475 -0.331716  ... -0.469176 -1.172991   \n",
       "2018-01-02 -1.917052  0.306130 -0.498284 -0.069999  ...  0.067329  1.704009   \n",
       "\n",
       "               股票192     股票193     股票194     股票195     股票196     股票197  \\\n",
       "2018-01-01 -1.363139 -1.203259  0.538908 -1.719053  0.366640  0.881434   \n",
       "2018-01-02 -0.516708 -0.189825  0.376453  0.099424 -0.439883  0.139151   \n",
       "\n",
       "               股票198     股票199  \n",
       "2018-01-01  0.762832  0.156397  \n",
       "2018-01-02  0.418381 -0.936374  \n",
       "\n",
       "[2 rows x 200 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df=df.transpose()\n",
    "df=df.T\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "28520093",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>股票0</th>\n",
       "      <th>股票1</th>\n",
       "      <th>股票2</th>\n",
       "      <th>股票3</th>\n",
       "      <th>股票4</th>\n",
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       "      <th>股票190</th>\n",
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       "      <th>股票192</th>\n",
       "      <th>股票193</th>\n",
       "      <th>股票194</th>\n",
       "      <th>股票195</th>\n",
       "      <th>股票196</th>\n",
       "      <th>股票197</th>\n",
       "      <th>股票198</th>\n",
       "      <th>股票199</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>0.195885</td>\n",
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       "      <td>0.205068</td>\n",
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       "      <td>-0.088246</td>\n",
       "      <td>-0.187599</td>\n",
       "      <td>-0.180987</td>\n",
       "      <td>0.002153</td>\n",
       "      <td>...</td>\n",
       "      <td>0.123196</td>\n",
       "      <td>-0.056761</td>\n",
       "      <td>-0.178860</td>\n",
       "      <td>-0.112299</td>\n",
       "      <td>-0.040427</td>\n",
       "      <td>-0.041517</td>\n",
       "      <td>0.036154</td>\n",
       "      <td>0.258533</td>\n",
       "      <td>0.054917</td>\n",
       "      <td>0.245758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-22</th>\n",
       "      <td>-0.094084</td>\n",
       "      <td>-0.062473</td>\n",
       "      <td>-0.176031</td>\n",
       "      <td>0.046001</td>\n",
       "      <td>0.065319</td>\n",
       "      <td>0.101453</td>\n",
       "      <td>-0.080533</td>\n",
       "      <td>0.008248</td>\n",
       "      <td>0.086029</td>\n",
       "      <td>0.039299</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.253372</td>\n",
       "      <td>0.167993</td>\n",
       "      <td>0.021743</td>\n",
       "      <td>-0.202781</td>\n",
       "      <td>0.415800</td>\n",
       "      <td>0.578365</td>\n",
       "      <td>-0.012838</td>\n",
       "      <td>0.142509</td>\n",
       "      <td>0.078496</td>\n",
       "      <td>-0.039550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-12</th>\n",
       "      <td>-0.100505</td>\n",
       "      <td>-0.006529</td>\n",
       "      <td>0.204549</td>\n",
       "      <td>-0.115918</td>\n",
       "      <td>0.106402</td>\n",
       "      <td>-0.031716</td>\n",
       "      <td>-0.246629</td>\n",
       "      <td>-0.500049</td>\n",
       "      <td>-0.186511</td>\n",
       "      <td>0.467269</td>\n",
       "      <td>...</td>\n",
       "      <td>0.113419</td>\n",
       "      <td>0.197252</td>\n",
       "      <td>0.019196</td>\n",
       "      <td>0.301393</td>\n",
       "      <td>0.363961</td>\n",
       "      <td>0.228003</td>\n",
       "      <td>-0.096328</td>\n",
       "      <td>-0.090518</td>\n",
       "      <td>0.186259</td>\n",
       "      <td>-0.275819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-05</th>\n",
       "      <td>-0.144519</td>\n",
       "      <td>0.130861</td>\n",
       "      <td>-0.359014</td>\n",
       "      <td>0.101067</td>\n",
       "      <td>0.003300</td>\n",
       "      <td>0.002513</td>\n",
       "      <td>0.077592</td>\n",
       "      <td>0.106891</td>\n",
       "      <td>0.160628</td>\n",
       "      <td>-0.143915</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.163528</td>\n",
       "      <td>0.070578</td>\n",
       "      <td>-0.042285</td>\n",
       "      <td>-0.201700</td>\n",
       "      <td>0.356702</td>\n",
       "      <td>-0.231311</td>\n",
       "      <td>0.101093</td>\n",
       "      <td>-0.054913</td>\n",
       "      <td>0.108909</td>\n",
       "      <td>-0.147813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-26</th>\n",
       "      <td>0.181953</td>\n",
       "      <td>-0.139300</td>\n",
       "      <td>-0.142456</td>\n",
       "      <td>0.139832</td>\n",
       "      <td>-0.352139</td>\n",
       "      <td>-0.089283</td>\n",
       "      <td>-0.055429</td>\n",
       "      <td>0.036664</td>\n",
       "      <td>0.355555</td>\n",
       "      <td>0.001382</td>\n",
       "      <td>...</td>\n",
       "      <td>0.161817</td>\n",
       "      <td>-0.062537</td>\n",
       "      <td>0.148582</td>\n",
       "      <td>-0.055231</td>\n",
       "      <td>0.019806</td>\n",
       "      <td>-0.242754</td>\n",
       "      <td>0.223056</td>\n",
       "      <td>-0.312107</td>\n",
       "      <td>-0.093648</td>\n",
       "      <td>-0.059568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 200 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 股票0       股票1       股票2       股票3       股票4       股票5  \\\n",
       "2018-01-01  0.195885 -0.361146  0.205068  0.362987  0.317622 -0.152646   \n",
       "2018-01-22 -0.094084 -0.062473 -0.176031  0.046001  0.065319  0.101453   \n",
       "2018-02-12 -0.100505 -0.006529  0.204549 -0.115918  0.106402 -0.031716   \n",
       "2018-03-05 -0.144519  0.130861 -0.359014  0.101067  0.003300  0.002513   \n",
       "2018-03-26  0.181953 -0.139300 -0.142456  0.139832 -0.352139 -0.089283   \n",
       "\n",
       "                 股票6       股票7       股票8       股票9  ...     股票190     股票191  \\\n",
       "2018-01-01 -0.088246 -0.187599 -0.180987  0.002153  ...  0.123196 -0.056761   \n",
       "2018-01-22 -0.080533  0.008248  0.086029  0.039299  ... -0.253372  0.167993   \n",
       "2018-02-12 -0.246629 -0.500049 -0.186511  0.467269  ...  0.113419  0.197252   \n",
       "2018-03-05  0.077592  0.106891  0.160628 -0.143915  ... -0.163528  0.070578   \n",
       "2018-03-26 -0.055429  0.036664  0.355555  0.001382  ...  0.161817 -0.062537   \n",
       "\n",
       "               股票192     股票193     股票194     股票195     股票196     股票197  \\\n",
       "2018-01-01 -0.178860 -0.112299 -0.040427 -0.041517  0.036154  0.258533   \n",
       "2018-01-22  0.021743 -0.202781  0.415800  0.578365 -0.012838  0.142509   \n",
       "2018-02-12  0.019196  0.301393  0.363961  0.228003 -0.096328 -0.090518   \n",
       "2018-03-05 -0.042285 -0.201700  0.356702 -0.231311  0.101093 -0.054913   \n",
       "2018-03-26  0.148582 -0.055231  0.019806 -0.242754  0.223056 -0.312107   \n",
       "\n",
       "               股票198     股票199  \n",
       "2018-01-01  0.054917  0.245758  \n",
       "2018-01-22  0.078496 -0.039550  \n",
       "2018-02-12  0.186259 -0.275819  \n",
       "2018-03-05  0.108909 -0.147813  \n",
       "2018-03-26 -0.093648 -0.059568  \n",
       "\n",
       "[5 rows x 200 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df.head()\n",
    "#在resample()函数中how已经不再使用了,改成  .median()\n",
    "df_20=df.resample('21D').mean()\n",
    "df_20.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ffd179a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2018-01-01   -1.043429\n",
       "2018-01-02   -0.021399\n",
       "2018-01-03    0.020054\n",
       "2018-01-04    1.000000\n",
       "2018-01-05   -1.084019\n",
       "Freq: D, Name: 股票0, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stock0=df['股票0']\n",
    "print(type(df_stock0))\n",
    "df_stock0.head()\n",
    "#Series是pandas中一个非常中亚的累，可以简单地理解Series是只有一列数据的DataFrame对象。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0d92aa23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#本质上pandas在给予封装Numpy的数据操作上海风撞了Matplotlib\n",
    "df_stock0.cumsum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4dddc32e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>-1.043429</td>\n",
       "      <td>4.113581</td>\n",
       "      <td>-2.492146</td>\n",
       "      <td>4.113581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-22</th>\n",
       "      <td>5.159171</td>\n",
       "      <td>5.159171</td>\n",
       "      <td>-2.070533</td>\n",
       "      <td>2.137825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-12</th>\n",
       "      <td>2.347429</td>\n",
       "      <td>2.347429</td>\n",
       "      <td>-2.831391</td>\n",
       "      <td>0.027222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-05</th>\n",
       "      <td>0.691791</td>\n",
       "      <td>0.691791</td>\n",
       "      <td>-5.691383</td>\n",
       "      <td>-3.007675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-26</th>\n",
       "      <td>-1.737121</td>\n",
       "      <td>5.022277</td>\n",
       "      <td>-2.096531</td>\n",
       "      <td>0.813328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                open      high       low     close\n",
       "2018-01-01 -1.043429  4.113581 -2.492146  4.113581\n",
       "2018-01-22  5.159171  5.159171 -2.070533  2.137825\n",
       "2018-02-12  2.347429  2.347429 -2.831391  0.027222\n",
       "2018-03-05  0.691791  0.691791 -5.691383 -3.007675\n",
       "2018-03-26 -1.737121  5.022277 -2.096531  0.813328"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stock0_5=df_stock0.cumsum().resample('5D').ohlc()\n",
    "df_stock0_5=df_stock0.cumsum().resample('21D').ohlc()\n",
    "df_stock0_5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b3b55340",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "DatetimeIndex(['2018-01-01', '2018-01-22', '2018-02-12', '2018-03-05',\n",
      "               '2018-03-26', '2018-04-16', '2018-05-07', '2018-05-28',\n",
      "               '2018-06-18', '2018-07-09', '2018-07-30', '2018-08-20',\n",
      "               '2018-09-10', '2018-10-01', '2018-10-22', '2018-11-12',\n",
      "               '2018-12-03', '2018-12-24', '2019-01-14', '2019-02-04',\n",
      "               '2019-02-25', '2019-03-18', '2019-04-08', '2019-04-29'],\n",
      "              dtype='datetime64[ns]', freq='21D')\n",
      "Index(['open', 'high', 'low', 'close'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "# 使用insert 0即只使用github，避免交叉使用了pip安装的abupy，导致的版本不一致问题\n",
    "sys.path.insert(0, os.path.abspath('../'))\n",
    "from canna import ABuMarketDrawing\n",
    "import canna\n",
    "canna.__version__\n",
    "\"\"\"\n",
    "WARNING: `mpl_finance` is deprecated:\n",
    "\n",
    "    Please use `mplfinance` instead (no hyphen, no underscore).\n",
    "\n",
    "    To install: `pip install --upgrade mplfinance` \n",
    "\"\"\"\n",
    "ABuMarketDrawing.plot_candle_stick(df_stock0_5.index,\n",
    "                                   df_stock0_5['open'].values,\n",
    "                                   df_stock0_5['high'].values,\n",
    "                                   df_stock0_5['low'].values,\n",
    "                                   df_stock0_5['close'].values,\n",
    "                                   np.random.random(len(df_stock0_5)),\n",
    "                                                    None,'stock',day_sum=False,\n",
    "                                                    html_bk=False, save=False)\n",
    "print(type(df_stock0_5['open'].values))\n",
    "print(df_stock0_5['open'].index)\n",
    "print(df_stock0_5.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "7be264d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20210707</td>\n",
       "      <td>62.690000</td>\n",
       "      <td>63.410000</td>\n",
       "      <td>62.090000</td>\n",
       "      <td>62.370000</td>\n",
       "      <td>62.900000</td>\n",
       "      <td>-0.530000</td>\n",
       "      <td>-0.842600</td>\n",
       "      <td>523167.540000</td>\n",
       "      <td>3272128.188000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20210706</td>\n",
       "      <td>62.420000</td>\n",
       "      <td>63.140000</td>\n",
       "      <td>61.850000</td>\n",
       "      <td>62.900000</td>\n",
       "      <td>61.910000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>1.599100</td>\n",
       "      <td>685812.850000</td>\n",
       "      <td>4296674.949000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20210705</td>\n",
       "      <td>61.990000</td>\n",
       "      <td>62.280000</td>\n",
       "      <td>61.130000</td>\n",
       "      <td>61.910000</td>\n",
       "      <td>62.300000</td>\n",
       "      <td>-0.390000</td>\n",
       "      <td>-0.626000</td>\n",
       "      <td>750646.750000</td>\n",
       "      <td>4629283.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20210702</td>\n",
       "      <td>64.030000</td>\n",
       "      <td>64.070000</td>\n",
       "      <td>62.100000</td>\n",
       "      <td>62.300000</td>\n",
       "      <td>64.760000</td>\n",
       "      <td>-2.460000</td>\n",
       "      <td>-3.798600</td>\n",
       "      <td>1028081.620000</td>\n",
       "      <td>6464701.802000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20210701</td>\n",
       "      <td>64.260000</td>\n",
       "      <td>65.170000</td>\n",
       "      <td>63.580000</td>\n",
       "      <td>64.760000</td>\n",
       "      <td>64.280000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.746700</td>\n",
       "      <td>692617.350000</td>\n",
       "      <td>4449223.158000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ts_code trade_date      open      high       low     close  pre_close  \\\n",
       "156  601318.SH   20210707 62.690000 63.410000 62.090000 62.370000  62.900000   \n",
       "157  601318.SH   20210706 62.420000 63.140000 61.850000 62.900000  61.910000   \n",
       "158  601318.SH   20210705 61.990000 62.280000 61.130000 61.910000  62.300000   \n",
       "159  601318.SH   20210702 64.030000 64.070000 62.100000 62.300000  64.760000   \n",
       "160  601318.SH   20210701 64.260000 65.170000 63.580000 64.760000  64.280000   \n",
       "\n",
       "       change   pct_chg            vol         amount  \n",
       "156 -0.530000 -0.842600  523167.540000 3272128.188000  \n",
       "157  0.990000  1.599100  685812.850000 4296674.949000  \n",
       "158 -0.390000 -0.626000  750646.750000 4629283.875000  \n",
       "159 -2.460000 -3.798600 1028081.620000 6464701.802000  \n",
       "160  0.480000  0.746700  692617.350000 4449223.158000  "
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mAPIaNEBBWBjqA0jZsgV3PTzQdvRoKHQ6nF+1CsVNmwIAwpYuRQMAd3r2ROLp00ZtKYKC0NHDA4pz53B6927ogoIcchwRJ0+iLoBEQcAdN/ndnHZ+CTZQUlIiFBUViX+PHTtWSElJEf/eu3ev8Prrr1ts4/jx47bsskLkbq9CvvpKEABBmDZNEDp3Zs937pSlaacfiwOhY6l6VJfjEIQafiyLF7P7zuOPC8KqVez5yJGC8Nxz7DkgCIMHs/fm5gpCkyZs27Ztptvr0YO9vnGj445j2DC2jz/+sGsfzsKZOmWTO3rdunX4+OOPAQBpaWnIy8vD+PHjkZqaCgA4fPgw2rRpI/9IwdEkJAAnTlj3Xh4P7tLF4NohlzRBEM6CJ2U1bw507MieHzoEfPste+7nB2zbBmzdCjz8MHDjBptS2a+f6fZ69mSP+/c7rs/kjjaLTe7ocePG4fXXX8fkyZOhUCgwf/58FBYW4plnnoG3tzeio6MxYcIER/XVMRw+DAwezOaxnT8PNGtm+f1chO+5B4iIYAkN//zj+H4SBEEAhulJLVowIfb1Be7eZdtGjQJ69ABmz2ZJWIWFQFAQ8McfgIeH6fZ69QLmzwf27XNMfwWBGToAJWaZwCYR9vT0xMKFC8tt79Gjh2wdcir79gHDhhnm+n7zDWDi+ETy84ELF1id1o4d2QmuUABHjwJFRYCPj1O6TRBO5fhx4JlnWHEHLy+gf38215RwDVJLWKUC2rcHjhxh2958k/29ZAmzgBUKNg+4eXPz7d13H5u7GxcHFBQwSzozE/D3Z8U1ONu3s0IbMTG29TcpCcjOZolhdeva9tkaQM2dNX3mDKsak58P9O3Lti1fzsTUHCdOsOkB7doB3t5ASAg74UtLDRcBQVQ3FixgA824OOb2fP99Ni2GcD7FxcyqVKmAJk3Ytk6d2OOgQcxD5+XFph75+TFP3eDBltsMDGRGhVbL7mNHjwINGwKjRxuqaf39N2vnnnuA559nYm0tPHO7UyfLc5lrKDVXhL/+mlmyEyawylddurAby9q15j8jdUVzHniAPf71l+P6ShCuoqDAcG5v2wZER7Pn3L1IOJdr15gwRkUZrNTnnmP3sS+/NLxv6FC2uMPzz1vXbq9e7HHHDmD6dCb2W7cCv//ODI85c9jrgsD2064dcPasdW3zil58sEAYUTNFWK9nMRIAeO01NqqcOZP9/fXX7ETbsgVYtoy9l3PoEHvs0sWwbeRI9vjnn86pwUoQzmTrVjZY7daNWVrcrZmY6Np+1VR4PFjqXm7Zks3zLetytsXq5MlZCxeykBuvnvXyy8D33zNrNjyc3QM7dmSu7r59rSuZSSJskZopwkeOAKmpQOPGhuzCiRNZAsPRo0xkhw8HnniCCTHAlv364w8m2P37G9q67z7mlr5yxfolxQjCXeDlDnmlJV6ViSxh1yCNB8sJF2Gtlon3tm3M2r15E3jqKfba++8D3buzZNZhw4CMDJZxLa0gaAouwvxeSxhRM0V4wwb2OGaMYbTo58fS+QEW+w0IYM9fe40J9iuvMKt45kzmCuKo1eyEBICNG+Xt5/nzrNycpTg1QTiKoiJg82b2fOxY9kgi7Dq4hw4A5J4KWrcum8YEMBd2z57AF1+wv3keTGws+9vbm91DR49mCVeDBrHFIUyRkcG8Jr6+8g8cqgk1T4QFwSDCDz5o/NqcOSyWMm8em9c2eLDhJNu+nVnKb79dvs1Ro9ijnCJ88SKzsidMYG6g119nJzRBOIvt21lMuEsXQxJQRAR7JHe089mzBzhwgHnexo+Xv/0vvmDGxocfsr/79gWmTGGZ0wsXMi8gx9OT5c8MG8ZyaUaMgNLUinK8Qlf79safJ0RqngifPs3iGaGhzLUipUEDNtJ87z2WMbhkCZt2xGtFz51rei3MgQPZSXnoEJCebn8fMzOBESOAnByW1p+ZCXz8MfD00/a3TRDWUtYVDZAl7CoEAXjnHfb8pZfY/UluBgwAPv3UeDWln35iU4wGDCj/fg8PYNUqZpVfvIioN94ANBrj91A8uEJqnghzK3j06IpHZk2aAG+9ZXj+7LOm3xcQwGIjUndRZdFqmfV79SrQoQO72XELu6LYC0HIRXGx4bzjrmiARNhV7N3LKlrVqmX+PuQI1GpmnJgjMBDYtAmoXRtBhw4xT8ncucCtW+x1igdXSM0RYUFg7rUffmB/jxlj3edeeYUV8di8mc2/MwfPkv74Y5ZRuHQpW+nEVtavB3btYotvb9zIYtVDhjBL+8YNIDfX9jbLsnMnE/joaODddw0l5QiCs2MHm+LSsSPw30IAAICwMHZjTktjQk04HmdYwfbQpAmweTOKmjRh+TMffsjcz9evkyVsBTVDhNPTWZ3nwYPZCK1NG6BPH+s+q1YDTz4JtG5t+X2jRjGRvnwZWLSIZRR+/bXtff33X/b4zDOG+Jtabdj/uXO2t8nJygKmTWOupTNn2EXyzjssS5x7CAgCMO2KBpj3iNf/5WvEEo5Do2Hr/u7b53wr2BbuvRfn165l/ezVi4XQRo1iuS0qFUvsIkxSM0R4wQLmygkJYc///de4HJscNGjAhG3lSsOc41WrbG+Hx587dDDe3r698eu2kp3NCousWMGyGz/6iFk7AwcCOp3lIiVEzaK0lM17B0wnAMnpkr54kQ1aS0vtb6u6kZfHpkr+/DOL065axZJDqyoKBcuq3riR1eA/d45lVrdqxe45hElsqh3ttvAFFn791VDhyhE0b87+jx7NLpxjx1iFG15lyBq4yHLR5fC/ra1SIyUvj3kBTpxgfdm61bBQRWgoa/voUdvbJaonu3axpMB27UxPK5ErQzozkw0Ck5JYQZC5c+1rr7rx3HOsXGTduizXRFqpryoTFMQqbXXrxrLrKR5skepvCefksLiEWl0+G9pR+Poapi3ZYGGqMzKY6zww0GBtcCprCV+4wG50R4+yNnfvNl4pqnVrFne+eVOezG7C/fntN/ZY1hXNkcMSFgTg8ccNLu1PPqHzT0pWFrB6NbMu9+xxHwHmtGnDLPcmTYCpU13dmypN9RfhQ4eYS+See5jYOIuJE9njr79a/RGfK1fYk/bty5ec4zGVM2esK4+Zn88m3bdrxyqENWzIBJhbMRyVylCGk7KvCY3GUNLVkSL87bcsDyEwkM2Hz8tjFZmqMufPAy++6JzBwqpVLLGzf3/5C3M4i5EjWd7JoEGu7kmVpvqLMF8jkxcodxaDBjG3zOnTLO5lBb5SES5LaChzS+XmWucGnDOHFVoXBJZYdvKkcaUvKV27skdySRN79jArrFUr88mI9rqjL1wAXniBPf/mG/ZfoWCP5iovuZobN5ggfv45K+bjaL7/nj0++qjj90W4lOovwjwe3Lu3c/fr5WWoyGWlNexjSYQVCuvjwnq9Idt55052c7O0jme3buyRZ2ZnZLB4VHy8Vf0mqgmCYFhP25wVDNhnCWs0rPxhcTErEzt5MvPWPPwwmyNvqiKdq7l7l+VUpKayv3/6iU3RqiwajeWlAE+eZP9r1WL5JUS1pnqLcGEhc7Eqlczl5WwmTWKP1oowtwLKZkZzrI0LnzrFbhjh4dZNxeKW8L//GuYkLl4MzJ5tRa+JasP337NEoJAQy9XZuCWclGS8ypg1zJ/P1iWOiDBeeu+119jjnj22tedocnJYhvLly+z6GzSIuYkXL658m7GxLDx0+7bp17kVPHUqZRXXAKq3CB85wkbXHTu6JrW/Xz9mEV+4YLrIxokTzOW3aROg0cD7+nW2vW1b0+1J48KW4FW7hg61bjmz8HCgfn3mhjx61FDQZOdONrWJqP4kJrJCEABbEL5+ffPv9fFhnhWNxmAdWkNcnCHu++OPxkUnGjdmj+nptgu7nAiCIefi7l3mgj56lA0atm41VNBbsoTlXdhKXh4ryJOTw6pglSUxkU1zBMgVXUOo3iLsKlc0x8PDcpGNRYuYQD/1FHDiBJRaLZtC5O9vuj1r3dF8EXa+ulNFKBQGl/STTzIPAsBusps2WdcG4b7odOyGn5fHQijcg2MJW13SV64w16pWy0Idffsav+7lxdyvOp3rFipJSmIWav367Dvo3ZsNHKKi2L2kQQPmUbv/fjZgnTmTHYeXF7OW9+2rOGnyn3/YdwAYJ0JqtcD//sfuF9nZbD/mPGJEtaL6irBGw7KBAecnZUnhVm1ZES4qMhRESElh4geYjgdzWrdmrvVLl8yXDLx7l43cPT2ZJW4t3CXNrWwez16/3vo2CPdDp2MVmXbuZIuFfP21dd4TLsKJiczLwwduprhwgQlacjITsPnzTb+PW9+2WNdy8sorzEWcns5CSOfPs2tu/36DpQ4YwjS//MKs2dJS5n3q3ZsltE2axIrh3L1bfh87dhieHz9ueP7SS+x/QQErkELXXY3BvUX46FFEvvee8ZSBo0eBzp3ZdKQDB9g2vmC1KzAnwtu3M3dWvXrsb+mSX+bw8WHFE3Q64NVXTSd3bNvGRuO9e5u3qE3BRRhg7nse89q2jVlI7kBpqfv0tSqg0wHTpzMx8fNjU5NCQ637LI8Lz5oFBAez+aCmvvuEBJaXcPs2e9y2zXiVHimuFOE9e9icfh8f9nzJEjbDgFvAUkaMYAllffqwqVZXr7KEstq12QD511+BN980nWQmFeETJ9hvoNOx3wBg5ULXrrUcDiCqFe4twn/8gTobN7IRLGDIvDx5kj2PimLTCWrXdl0feRy3rAuZF/F4+WV2UXMqckG9/DKzhhcvZvMHd+0yft1WVzRHWgzglVeYW+6++1gSCm/Tmej1zJNhaRGMrCxmwc2fzxJmatVig5oLF5zXT3dm/nxWxtTPj8U7e/Sw/rN8cJmRwQZ96eksB0OKIACPPcZe69ePWYuWBoZceOzJPK4MGo2hJvMbbzBxnTmTLcZiaulSpZLN492zhxUciY5myYzJyczF/MEH7H3cCOAkJ7Nz09+fXV8FBcDFi/C9cIGdy1FRxitWETUDwckcP35cvsauXxd0Hh6CoFAIQlycICxZwtIqmjUThNxc+fZjD4mJrE+1awuCXs+2FRYKgp8f2379uiCcPy8IKhX7+9q1its8dkwQOnVi71epBOHLL1nbCQmCEBLCtl++bHtfZ84UhOHDBaG0lP29aBFra/x4m5uy+3f++GO279GjDd8bJy9PEMaN4yk05f+/9JJ9+y6DrOesNRQVCUL//oIwZoyszZY7jnvuYd/Xb7/Z3lhpqSCsXSsI+/cLwlNPsXbee8/4PcuWGc79tLSK23zxRfb+Tz+t8K2y/iZffsn2GxXFvnt7KSgQBLVaEJRK4/vQ8uVsP8OHC8LYsez58uVC8syZ7PlTT9m/bxfi9OvEgch9LJbac28RFgTh9tSp7ATu0UMQ6tWr/E3FUej1ghAUxPp1+zbbtmED+7tLF8P7Vq8WEm0RD41GEN580yA8995rEPK2beXp+82brD1fX5sHNXb9zhkZhu8MEIQffjC8duuWYQDi5cWOe9YsQVi9WhA2bmTbw8IEQaut/P7L4PSbi/R3TU+XrVmj4ygpEQRPT7aP7Gz7Gl69mrUzdKhhW1KSIAQGsu2rVlnXzoIF7P0vv1zhW2X9Tdq1Y/tdt06+NmNiWJu7dxu2TZnCtn35pWGQOWuWkNu5M3u+fr18+3cBJMKVa8+93dEAUmfMYG7IAweY26tbt6rl0lEoyseFuSt6wgTD+yZNQvqUKda3q1Yzt9fKlSw7k7sCJ082JHzZS2Qki6cXFrJEk7JotSx2NmUKi2W3bm398naCALz3HvDZZ+Vf++QTNoWjYUP2Ny8c8t13LHZ98iRb4/bMGeDwYTalZtIklqEaHc3ij1Vtvqm1nD7NVvriOKpgSnw8i6E3a2b/9D1ek/3IEUN28IsvsoStkSOty7YGXBMTTklhoSJfX3b+yMW997JHfl3q9YZ48IABhlKxe/bA//Rp5uK2JZGSqDa4vQjrAgONy8h9+ql12Z3ORCrCOTlsqS/AclUia5kyhQnRhx+yBJFVq8yXp6wMn37KHhcuZAULOILAxHHWLFZo/uxZFu9assS6drdtY4krs2ezeCTn9m1DEYfff2ffUX4++w4ff5ytB33//eyYy67wo1AYisXzRBd3QRBYxvujj7LBjZcX216ZVbOsgWfmcjGwh4gIJqCZmewcvHuX/XYqFTsfrL0eXSHCf//NHvv0MXznciAdmABswHjnDpuT36IFEBPDtp8/D4VOx4yH4GD59k+4DW4vwgBYdZ9x41jGsCszoc0hTc768UdmWfbtyzJK5aBTJ5ZQIp1GIRfdurEMWo2G1fvlls5nn7HpLF5ezBLlBT6WL2fvtYRez/rLefpp9p0IAhPmoiJgzBiWLPbNNwaL+J57mLju2WM6YQYAHnqIPa5fb3naTFXhwAGWYKdUsszcuDigUSPDsn6m5pfLQVwce+RiYA8KhbHlt349y/gdMMDw21kDz8x2hQjLvciA9PsQBJYABwBDhrDvKzjYeDWzAQPk3T/hNlSP9YS9vAzLr1VFuCV85oxhQQmejekOzJ/PbqxbtzJLU61m6yUD7OYyfjy70Xz6KbOGt2xhQmKOtWtZac2GDVmJxLNngddfZ5m2K1cyQeIZprVrM8G4c8d8JTEpzZqxgcPRoyyDPDKSCcLkyazdqkJJCau+9OmnhoGNWs1uzsuXG95nSoSLioClS9n3bovISZHTEgaY6PzxBxMdnp3OVxKzFmdbwlIXsdwiHBXFBorp6ew35KUoeT0AgA0qeb34gQPl3T/hPtgaYB49erQwdepUYerUqcJrr70mnDx5Uhg3bpwwceJEYfHixXYFqCuDWyQD3L1rnL3bqBFLrCpDlT6Wzz8vn4VcNot14UK2fdiw8seSkiIIFy8KQk6OIDRtyt737beCcOgQy27nbfr6Wp/IY47Fi8v39ddfK92c7L9LRgZLJARYBu0bbwhCcbHxe1JT2euBgeWzw+fMYa+1a8cy7a1EPI7iYkHgswpycuw8mP/Yu5f1KTKStevpKQhZWba1odWy7wNgiWMWkOU3OXbM0Oey37EcDB/O2u/Zkz3ef7/x6//NPtD6+RlmJLgxVfr+ZSNVNju6uLhYGDVqlNG2kSNHCgkJCYJerxcee+wxIT4+vtKdqQxu88OHhRkEYf58k2+p0sei0zEh+/pr9n/HjvI3rvR0dnNXKoXTW7YYth8+LAg+Psai2Ly5YSDy9NOGrO4LF+zva2amIHTrxtrr0IG1HRtb6eZk/V1u3hSEVq1Ynxo2ZIMQc9Sty96XmGjYdueOYXobIAhPPlnxPouLBWHzZuH6O+8woTt+nH22RQv7j4eTn28QUEAQytwnrKZ+ffb5pCSLb5PlN/ngA7avJ56wvy1L7fP/a9cavx4fLwgeHkL66NGO2b+TqdL3Lxtxpk7Z5I6+ePEiioqKMGPGDGi1Wjz77LMoLS1FxH/Vc3r06IFDhw6htbl1SGsybduypCMvL1bAwN1QKo2zuU1Rty4rd7l2Ler+9huLf127xoqRFBWxQhpZWcw9/NlnzP0KsESs8eOZG9nHx/6+1qplSIg5f57FXLdvZ+5HZ7mkBYFVRGra1JB9nJbGkspu3WLnw9atLFHHHG3bsvj32bMG9/7nn7MiDx07Mrfv0qUsv4C7fjUaFmPfsIHFWENDmes5Px9NAFYoghfMkMsVDbCCH+3bszADYLsrmlO/PnNHp6VZ/m7kYPt29uioRed5XBhgx1J2WcLWrYFbt5B07RosLDRKVHNsEmFvb288+uijGD9+PG7evInHH38cgZKVUPz8/JBkxRSVOJ4UIhNyt+cIGtavj/oA7g4ciISEBLOF793hWCwR0Ls3mq9di7Aff0TB0aNQ5eXB++5d5Nx3H64uWgQolVCUlkLw9jYkBwFAQAATTLkRBLQLDYVnWhrOr16NopYtK9WMLb+LMj8fkfPnI2T7dhQ1boyLP/0EvZ8fmrz+OkJu3UJ++/a4+sUX0KWlWawO1ahePdQDkLxtG9JCQ6HKzUW7zz+HCsDF556D76VLiFiwALrp05F85gwyRoxAk7lzUYtXUcvMFOOzRU2awOfGDejmzkV+x44IApBUrx7SZTzfGjVtinqnTkHv5YXT4eHQV6Ltpr6+CAJw5cABmFh3zAh7rhVlfj46HjoEqFQ4Vbt2pfpa4T48PNBRoYBCEHBr1Cikmlv9zMPD7a97TnU5DsCJx2KLSV1SUiIUSSrKjB49Wujbt6/4948//ih89913lTbLK4PbuEBSUwXhrbeYO9EMbnMsltDrBWHRIqE0ONjghuvUybUVzB5/nPXjww8r9XGTv4tez9yNI0cKwooVrErS7dvseePGxm7I8eMF4Y8/2HM/P0G4ccO6HS9dauxKf/tt9vcDDxj6MGOGYT885BEUJAh79gjC6dOC8NdfrCqbIAgZAwca92vfvkp9H2ZZu5a1O2VK5dt45BHWhiPvIzqdIEyYwPbTq1fl27GGYcNY2KG6X/dC9TkOQajCxTrWrVuHjz/+GACQlpaGoqIi+Pr6IjExEYIg4MCBA+gip4urOhEaCrz7rvmpNdUFhQJ48UWc3byZTWF67DGWLR0Q4Lo+DR7MHrdtk6c9QWDzm+fOZXO+Y2NZFndYGHt+8yab+rN5Mzvu334zFKz46CPrp5JJ55ffvs2WugMMa9oqFKyAyerVbP+3b7M1ev/+m817bd+ehQT+mwqX9PLLhrmoCgWb2iYn48axY7Z2rrgpHJUhrdMZhh8vvcQy9AMDDXPSHcWmTcD169X/uicqjU3u6HHjxuH111/H5MmToVAo8NFHH0GpVOKVV16BTqdDjx490IHWwCQA5m5+6ilXd4PRvz8rHHHoECuWYk+FKEFg89EXLmTrRb/yCovbHjnCqi717MmEb+ZMtpzkjz+yCm7FxSxGOGuW9fviInz+PCuMkpvLFuaQzoVXKJjA9+sHLFvGKlTxeell0NauzWLxjz3G3mPLKlvWoFDYvnBIWRwhwmfPsli8VssGStevs9/u998dv2avQsHOA4Iwg00i7OnpiYULF5bbvpaXYSSIqkhQEFsRav9+turUmDGVb+uXX5iQeXgwC3fUKLY9PZ1ZmWVvuGPGAO+/z+ZV//ADGwxYS2Agq0aVmMiWuPPxYYVRTFGvHls+ryJmzGDJgWaE2uWYK9iRnc0qcUVH214R79NPDcssXr/OPv/zz1QmkqgSVKHqBQThQLhLetMmQ3EMWyktNawRu2SJQYABJoLmLJ65c1nJz1atbN+ntEDJO+/YXxWNl/asqh4rqSXMvQ7Nm7OM92bNmKWfkWF9e+npbH1fhYLV5T53Drh40fp61gThYEiEiZrBkCHs8ccfmVW8fj0TVVtYvhy4cYOJ6fTpsnfRJNxibdeOLYpQ3ZGK8D//MCv2yhVmvfv6sphzx47wO33auva+/579zsOGsRh5mzbla44ThAshESZqBp06scSmkBAWvx03js1rnjKFxXe/+oqVzCwpMf354mJDKc133rHNrWwPM2cCjzzCrDkPD+fs05VIRfirr9jz2bOZO/n8eRZXT05GMx4jt4RWy5IDAeCZZxzXZ4KwAxJhoubwwgssvvrll8wqys1lmcWvvMJu0lOnGhZOkKAsLGRx4ORk9jk5Vr+ylshIZoFXxpXtjgQFMas3P58lTqnV7Hfz8GDfxb59wL33QlVQUHG9+M2b2dKaTZvSAglElYVEmKhZ+PmxxTNOn2bL7i1axNy8jz/OXl+8mAk1wJZLbN0anXr1MiyX+e67VWshiOqGQmGwhnU6VoGtQQPD6x4ehkUQpAtdmIJb0rNm0W9GVFmqxypKBFEZoqON46z5+cwyfvttltE8ejSQng69Wg1l69bMApYmYxGOoX59Q0U5U27kceOge/ppqA4eZAlvpmK8168DO3cC3t7MnU8QVRQaHhIE5/33Dcs0Dh7MMmv798ep/fuZ5Txvnu3TYwjb4ZZw27am1wf390fWAw+w5z/+aLoNbiWPG2coUEIQVRASYYLgREczV6deD8THsxjkmjUQakJCVFWCZ4S//LLZQU/GyJHsyU8/sYz1995j617r9cyNzUXYHRdLIWoU5I4mCCnz5jFLWKNhKxHVqWN2sQ3CQbzxBnP7WyiBm9+xIxs0XbsGREUZXhAEtsLUrVssIatXL4d3lyDsgSxhgpASGspWdzp1Cujc2dW9qZn4+FS8zKJCATzxBHvu6QkMH86ez53Lst0BVh2MwgdEFYdEmCDK0qwZ0KKFq3tBVMTLL7M1gW/dYpXQ3nmHWcIXLrBs6IcfdnUPCaJCSIQJgnBPVCpg4EDDCkXz5gEjRrDnQ4caT20iiCoKxYQJgqgeKJWs6tmyZc4tqEIQdkAiTBBE9SEggK0XTBBuArmjCYIgCMJFkAgTBEEQhIsgESYIgiAIF0EiTBAEQRAuQiEIguDMHcbFxTlzdwRBEAThcmJiYkxud7oIEwRBEATBIHc0QRAEQbgIEmGCIAiCcBEkwgRBEAThIkiECYIgCMJFkAgTBEEQhIsgESYIgiAIF0EiTBAEQRAugkSYIAiCIFwEiTBBEARBuAgSYYIgCIJwESTCBEEQBOEiSIQJgiAIwkWQCBMEQRCEiyARJgiCIAgXQSJMEARBEC7CZSJ8+vRpxMbGWnzPhg0bMH78eIwZMwZfffWVk3pGEARBEM5B7YqdLlu2DBs3boSPj4/Z9yQmJmL16tVYsWIFPD098eWXX0Kj0cDDw8OJPSUIgiAIx+ESSzgiIgKLFy8W/7506RJiY2MRGxuLZ599Fnl5eTh06BDatm2LOXPmYOrUqejcuTMJMEEQBFGtcIklPGjQICQnJ4t/z5s3Dx999BGaNm2K3377Dd999x28vb1x/PhxrF69GiUlJZgyZQo6duyIwMBAV3SZIAiCIGTHJSJclmvXruHdd98FAGg0GjRu3BgdOnRA165d4e/vD39/f0RFReHmzZto3769i3tLEARBEPJQJUS4SZMmWLBgARo0aIC4uDjcuXMHTZo0wapVq1BSUgKdTodr164hIiLC1V0lCIIgCNmoEiL8zjvvYM6cOdBqtVAoFPjwww/RpEkTjB07FpMnT4YgCHj66acRHBzs6q4SBEEQhGwoBEEQXN0JgiAIgqiJULEOgiAIgnARJMIEQRAE4SKcHhOOi4tz9i4JgiAIwqXExMSY3O6SxCxznakMcXFxsrbnSuhYqibV5Viqy3EAdCxVkepyHID8x2LJ+CR3NEEQBEG4CBJhgiAIgnARJMIEQRAE4SJIhAmCcDkanQZ9fuyDt/a85equEIRTIREmCMLlXMq4hH8S/sEvZ35xdVcIwqmQCBME4XJyinMAACW6Ehf3hCCcC4kwQRAuJ6eEiXCprtTFPSEI50IiTBCE1Zy/cx6nUk/J3m5uSS4AoERLljDhHsTGxuLatWt2t1MlVlEiCMI96P9zfxRqCpHxagbUSvluH+SOJmoqJMIEQViFXtAjNT8VAFCkKUKAV4BsbUvd0YIgQKFQyNY24b4MWzUMf135S9Y2hzYbii1Ttph9/ZlnnsG9996LmJgYnD17FosXL0ZgYCCSk5Oh0+kwffp0DB06VLb+kDuaIAirKNIUic/ltli5JQwAGr1G1rYJwhbGjx+Pffv2AQA2bNiAXr16ISQkBGvWrMHy5cvx+eefIzMzU7b9kSVMEIRVFGklIixz7JZbwrxtT5WnrO0T7okli9VR9OzZE++99x6ys7Nx/Phx6PV69OjRAwDg7++P6OhoJCUlybY/soQJgrAKh1rCEhGmDGnClSiVSnTr1g3vvPMOHnjgATRr1gzHjx8HAOTn5+Py5csIDw+XbX8WLWGNRoM33ngDt27dQmlpKWbOnIn+/fuLr+/evRtfffUV1Go1xo4diwkTJsjWMYIgqhaOtIR5djRAyVmE6+nTpw9efPFFbN++HfXq1cO8efMwefJklJSU4JlnnkHt2rVl25dFEd64cSOCg4Px6aefIjs7G6NHjxZFWKPRYP78+Vi3bh18fHwwefJk9OvXD3Xq1JGtcwRBVB0KNYXic0fGhGmaEuFqateujfj4ePHvBQsWlHvPihUrZNmXRXf04MGD8fzzzwMABEGASqUSX7t27RoiIiIQFBQET09PxMTE4NixY7J0iiCIqoeRO9qBMWFyRxM1CYuWsJ+fHwDmB3/uuefwwgsviK/l5+cjICDA6L35+flW7dTSAseVQe72XAkdS9WkuhyLPcdx+u5p8fmZ82egTpMvr/NO7h3x+cmzJ5EfWPG9pLr8JkD1OZbqchyA846lwqvo9u3bmDVrFqZMmYIRI0aI2/39/VFQUCD+XVBQYCTKloiJialEV00TFxcna3uuhI6lalJdjsXe40i9nAocYc8bRzdGTLR830nRLoOVHd08GjENLbddXX4ToPocS3U5DkD+Y7Ek6Bbd0Xfv3sWMGTMwe/ZsjBs3zui16OhoJCQkIDs7G6WlpTh+/Dg6deokT48JgqhyOComLAiCUWIWuaOJmoRFS/ibb75Bbm4ulixZgiVLlgBgE5mLioowceJEvPbaa3j00UchCALGjh2L0NBQp3SaIAjn46js6CJtEbR6raFtyo4mahAWRXju3LmYO3eu2df79euHfv36yd4pgiCqHo6aJyzNjAYoO5qoWVCxDoIgrMJRlrA0MxogdzRRsyARJgjCKhwVEy5nCZM7mqhBkAgTBGEVjponXNYSJnc0UZMgESYIwiqM3NEyWqvSzGiA3NFEzYJEmCAIq3CYJUzuaKIGQyJMEIRVFGodFBMmdzRRgyERJgjCKpxlCZM7mqhJkAgTBGEV0piwnELJLWF/T38A5I4mahYkwgRBWIWjinXwxKy6vnVZ2+SOJmoQJMIEQViFo7KjuSVc14+JMLmjiZoEiTBBEFZhVKzDATHhen71WNvkjiZqECTCBEFYhcNqR3NLmNzRRA2ERJggCKtwWO3oYmMRLtWTO5qoOZAIEwRhFY62hEV3NFnCRA2CRJggCKtwVEyYZ0dTTJioiZAIEwRhFY7Iji7VlaJYWwy1Uo0g7yBxG0HUFEiECYKoEK1eC61eK/4tlyXM48FBXkHwUnnJ2jZBuAMkwgRBVIg0HgzIZwnzeHCQdxC81F6ytu0qtHotdl3fVe47IwhTkAgTBFEh0ngwIL8lHOgVKFrC7u6OXn9+PR5Y8QAG/jIQxdpiV3eHqOKQCBMEUSE8HqxSqADIZ63ypKwgryB4qjxZ227ujr6ZfRMAcCDxAB7+42HoBb1rO0RUaUiEK8n1rOsoKC1wdTcIwilw12qwdzAAGS3hauiOzivNE5+vjV+L13e+7sLeEFUdEuFKkJybjOaLm2PS+kmu7gpBOAVuCdfyqQVAxpiwicQsd3dHc+t+ZIuRUCvV+PTQp0gvSHdxr4iqColwJbiaeRU6QYdLdy+5uisE4RR4TDjIi00jktsSDvQKrDbuaG4Jj2oxCi3rtIQAAan5qS7uFVFVIRGuBHz0nl2c7dqOEIST4O5oPpdXo9fIEus0soSrizu6hIlwgGeA6L6newVhDqtE+PTp04iNjS23/ccff8SwYcMQGxuL2NhYXL9+XfYOVkX4BZVdnA1BEFzbGYJwAtwd7evhCw+lBwB53MZGMeFqMk+YW8KBXoEkwkSFqCt6w7Jly7Bx40b4+PiUe+3cuXNYsGAB2rZt65DOVVX4BaXRa1CkLYKvh69rO0QQDoZbwr4evvBSe0FTqkGJtgTeam+72jWVHV1dYsIBXmQJExVToSUcERGBxYsXm3wtPj4e3377LSZPnoylS5fK3rmqCh+9A3RxETUDHhP2UfsYLFYZ3MbSmHC1dEd7BQOg+wRhngot4UGDBiE5Odnka8OGDcOUKVPg7++PZ555Bnv27EHfvn0r3GlcXJztPXViexVxKcGQkHUw7iCiAqJka9vZx+JI6FiqHpU9jks32Tmfn50PpZ6N3Y+dPIb6PvXt6k9CWgIA4G7yXZwuPQ0llNALehw9dhRqpeXbU1X9TTLyMwAANy/fRFE28yCcv34ecWrz/a2qx2Ir1eU4AOcdS4UibA5BEPDwww8jICAAANC7d2+cP3/eKhGOiYmp7G7LERcXJ2t71uCV7CU+bxDVADER8uzfFcfiKOhYqh5xcXHo3Lkz5u2Zhx4RPTC46WCrP7u3ZC9wDogIi4B/jj/ulNxBi9Yt0DSkqV190saxetTd2ndDTMMYeG7zRLG2GO06trMY5qnKv0nxTlYl6/4u9+Oy6jJwBfAN8TXb36p8LLZQXY4DkP9YLAl6pbOj8/PzMXz4cBQUFEAQBBw9erTGxIad7Y7WC3r8dOonsRIPQVSWM2ln8OH+D/H6LtsKSEgTs7jbWI7YbWZRJgAgxCcEANw+OUsQBMqOJmzCZkt406ZNKCwsxMSJE/Hiiy9i2rRp8PT0RPfu3dG7d29H9LHKIb2gnHFx7b6xG4/8+QgmtJmAX8f96vD9EdUXfr7eLbxr0+dMxoRlEMqs4iwAEhFWewEl7hsXLtYWQyfo4KXygofKg0SYqBCrRDg8PBxr164FAIwYMULcPnr0aIwePdohHavKOFuEr2exqV93Cu44fF9E9Sa/NB+A7ectz4728fCRLYFKp9chuzgbCijEIiDuXjWLZ0YHegUCAIkwUSFUrKMS8AIDgHMurtt5twEABRqqVU3YBxfh/NJ8o/WBK4K7o+W0hPm1E+wdDJWSLQzh7lWz+BzhAC+WK0MiTFQEiXAlcLYlfDufiXDZ5eQIwla4CAPGg8mKMBUTttcSLhsPBuD205Sk8WCARJioGBJhGxEEwekizOvOkggT9iIVYR6PtQYxJuwhnyVsUoTd3B1NljBhKyTCNlKoKYRO0Il/Z5dkO3yf3BKmpRMJe5GKsC3CIMaE1fLFhLkI85WZAPd3R5eNCfNa2zklObSuMGESEmEbKXvjyiqy3pqoLDwmTJYwYS+VFmGtJDHLkZZwNXNHq5Vq+Hv6Qy/ojb57guCQCNtI2RuXo91MgiAYuaNpwQjCHuy1hB0SE/auhu7o/0QYIJc0YRkSYRvhhTpq+9QG4PgLK7MoExq9BgCgE3Rue3Miqgb5GklM2AYvjiPmCZuyhKubOxogESYsQyJsI/xCahzc2OhvR8HjwRxySRP2IKs7mrKjyyG6o73KW8K2ZKMTNQcSYRvhN67I4Ejxb0e6iLkrmkNzhQl7kCb32Z2YZae1WrZaFkDuaKLmQSJsI3w0W9e3Lnw9fKETdA4VRp6UxSFLmLCHyk5RcpYlXF3c0aYsYRJhwhQkwjYirfLjjIuL3NHWc/L2SSw8tJCmglhA1sQsB84Tdlt39H+WsFFMmNYUJixAImwj/EIK8gpyigiXc0fLPFe4VFeK7Ve3izdZd+bVna/ilR2v4GDiQVd3pcpSGREWBMEoMUu0Vh0YE3Zbd3QJuaMJ2yARthGeHV1dLOEfTv6AwSsHY/G/i2Vt1xWk5KUAANIK0lzck6pLZUS4VFcKAQI8lB5QKVWyZ0dXp2IdZStmASTChGVIhG3ElDvakQU7uCXMFziXW4SvZFwBANzIuiFru66AL8/njAIq7kplYsLSeDAgTwazIAgGEfY2iLC7u6NpihJhKyTCNiK6o72d447miVnRtaIByJ8dfaeQLY+YWZxptD2rKMutCoMIgoCMwgwAtiUc1SQEQaiUJSyNBwPyCGVeaR50gg5+Hn6iqAPV3B3thBK3hPtBImwjUnc0H8E7wx0dHcJEWG5LOL0gHYCx9bjnxh6EfBKCRYcXybovR5JTkiPW9CZL2DQl+hIIEKBUsMve2vNWGg8GIEtilql4MFA93dG8fnRNtoS/PPolXt/5uqu7USUhEbYRZ2ZHF2oKkVuSCw+lB8IDwgHIn5glWsJFBks47nYcAOD47eOy7suRcCsYqNk3O0sUapmY1vKuBQ+lB4q1xSjWFlf4uXLuaBksYXMiXF3c0ZSYZUCj02D2jtn4+ODHYsiIMEAibCPOFGEeD67vXx9+nn4A5LeE7xQwEZa6cLmgSYW5qiO9uMkdbRouwgFeAWIylDXnrrRQByCPJcy9FeVE2I3d0aW6UpTqSqFWquGt9ha3y3mfWHNuDep8UgdxKXF2t+UsrmZeFX9Pd7qnOAsSYRvhxTqcMUWJx4PDAsIckpglCILojpZeHFzQpNZlVYdEuGKKdExM/T39bTp3uSUsZ0y4QkvYDd3R0niwQqEQt8t5n9h8eTMyijKw+txqu9tyFufvnBefkwiXR+3qDrgTJdoSFGmLoFaq4evh6/CEC24Jh/mHwc+DWcJyJmbll+aLN9Ls4mzo9DqolCpkFLmfJcz7DFSPmPDKMyvhqfLE+DbjZWuTW8J8aT3AOmEQY8JlsqPtsVYrjAm7oTvaVLUsgA3YAUOJW6lA2woPH/17699Kt+Fs4u/Ei8+rw7UpN2QJ24A0KUuhUDjeEv4vKau+f32HWML8gubw43NHEa5OlnCJtgSP/PkIpv4+FRqdRrZ2K20Jl3VHy2CtmrWE3dgdbapaFgB4qDzg5+Eny5rCPHwUdzsOWr3WrrachdQSdvdr0xGQCNuA1BUNwOHzhEV3tH+YQ2LC3BXN4TdGLmg5JTluc6FLXefuPtrOKMqAVq9Fqa4UiTmJsrUrtYR5Zr8135Uj5gmbmiMMOC8xq1hbjKkbpmLDhQ2ytWlqehJHrgE7v2YLNYWIT4+v4N1VAyMRtuPazC/NR5slbdD/5/44kHhAjq5VCUiEbUCalCV9dHRiljQmLKc7mo+qOfwCcUdBk1rCjl7ZytFIv//rWddla7dQZxDhyljCvuoyMWE3nqK0P2E/Vp5diQUHF8jWpqnpSRw57hWCIBh5r9zBJa3Va3Ep45L4tz3etbNpZ3H+znnsvrEbPZf3xNCVQ93m/mQJEmEbKCvCjp4n7Gx3dGZRJit6IYmvuotLWtpnnaCz2+3nSqTHIqcIc4vW38M2ETYXE7bLEi52rTuaD3DLrlJmD6amJ3HkEOG80jyj78UdRPha5jWjPtvjjubXRX3/+vD39MfWq1ux+fJmu/voaqwS4dOnTyM2Nrbc9t27d2Ps2LGYOHEi1q5dK3vnqho8Zson3/PHnJIch6zcIxVhMTFLxnnCZd3RWcVZyCvNM3JBSwWhKlN2/qE7x56kxyKrJawtbwlb8z2J7mhnxISd5I7m9cVT81Nl85pwd3TZmDAgjwiX9Vz9m1L1RVialAXYd13y62JA1ADM6DjDaJsc3Mi64ZLwW4UivGzZMsydOxclJcYXhUajwfz58/HDDz9gxYoV+PXXX3H3bvWeiC1awv8tTaZWqsVMU0dYXvyiC/ULdYwlXFDeEi57UrubJcyrQXE3lSAIblckwcgdnS2jJVzJxCyHWMIudken5TMR1ug1sg3YRHd0BZbw4aTD+Ob4NzaLP/dcta3XFiqFCufSz8levEdueDw4qlYUAPvCW/zeVMe3jnjeyHV/+ufmP4j6Mgpv7HpDlvZsoUIRjoiIwOLF5VfYuXbtGiIiIhAUFARPT0/ExMTg2LFjDulkVaGsO1r6XO4bvSAIRiedI93RYf5hANgJXXZusLuIMP+uGgc3BmAYcc/dPRe1P6mNk7dPuqprNuMod7SpxCxrztvk3GQAbDAIVI/saOlKW2WXC60s5qYoAYb7xNn0sxi8cjBmbpmJfxL+sal9PmiOCIpAu9B20At6nLh9wr5OOxhuCfeI6AHAvvsJv8Zr+9SWXYQPJx8GYKgW6EwqFOFBgwZBrS4/nTg/Px8BAYaTzc/PD/n57huHswYxO/o/NzTgOBEu0BSgRFcCb7U3fD18xexoOROzuDu6RZ0WANgotaz72R0KdkgHLE1DmgIw/B4Hkw5CL+hxLMV9BoiOSsyqrCV8JZOttNWsdjMA8ljCZitmWeGO/uTgJ1h+dXml9w0Yi7BccWFr3NGLDi8SxXrjpY02tc+v17q+ddG1QVcAVT8uzC3hHo2YCNsVE/7vujCyhIvlEeGrmVcBQNbZCNZS6WId/v7+KCgwCEJBQYGRKFsiLk7e0Ya59hbFL4K3yhtPt3xalv1cTroMAMi7kyfuU61lX+HR00ehqW3/nE7ebkohWxs3SB2EEydOILOEnWy5RbmyfX8JdxMAACF6dkJfSb6C4OJgo/fE34hHnGfl9if372yOfE0+tHotfFQ+8CjxAACcvHgSjQoa4dqda6wvl+MQh8r3x1nHAgCXky+Lz7OLs7Hn8B4Eepa/sdsKj+3euXUH3tmsrOKtjFsVHtuF1AsAgOKUYsRlx4luVK1ei2PHj4khAGsp1hWjSFsED6UHLpy5YFS84lbhLQBAXmGeyX7lluZizs45AIChB4Yi1CfUpn1zbt65KT4/fPYwgrOCK9WOlOu32IApKzWrXN/z7zIDRSfo4KH0gEavwfqz6/FQnYcAWHd+nbp6CgCgz9Mj1J8d97Zz29DHq4/dfbeF5IJk7Ly9ExMbTxTzBDjS49Dqtbhwh507QXnMcEnPTa/0tXQ1hQllTmoOSlXMU3Iz7aYs1+bJBOYpS8xOxPHjx6FQKJx2zVdahKOjo5GQkIDs7Gz4+vri+PHjePTRR636bExMTGV3W464uDiT7eUU52DV5lVQQIFvJ38LlVJl9748E1m8ql2zdojpwPYZfjkcpzJPoXaj2ohpZd9xSY9FSBGA3UCD4AaIiYlhMecdQKlQKtv3V7CPDaJ6tOyBDYkboPRTIjCU3exVChV0gg5eQV6V2p+538URXM+6DmwH6vnXQ9OGTYFkICg0CJ07d8adrcyFpwhQVLo/zjwWAMBl4z8DIwMR08D+/RceY+7odi3aoWWdlsBBoFRl+XwqKC3Anc134KnyxLD7h4nXkdc2L5ToStC2Q1sxVmwtKXkpwFagtm9tdOnSxei1sLwwYDcgqAST/dp7c6/4PN0/HUM7DrVp35y8vXnic5+6PrL8vl43mRXfplkbxLQ3bu+k4iTA9Ag/jPoBz297HokFifCP9Ed+Qr5V+191dxUAoF1UOwxpNgTvn3kfVwqvOPfcBPDVn19h+cXl6Ny8M6Z3mi5uL3udXM64DI1eg0aBjTCk+xDgHyBfZ92xmkJ7hiVNdWvbjZ1z/wIatUaW40/7h3lGSvWliGgVgaSLSbLrlDlsnqK0adMm/Prrr/Dw8MBrr72GRx99FJMmTcLYsWMRGlq5Uakj4O4mAYJsiRdli3UAQIfQDgCAX878YvIzv1/4Hbtv7LZ5X9J4MACjmLAcmdjSOYfcHZ1ZlCm6o3kihTtkR3M3VW3f2kZFKDKKMkS3Js80dwfE4/GpDUA+l7Q4RcnT3+oFHLibLqpWlNFA1h6XtLlCHYDBHW0uJnwq9ZT4fOf1nTbvGwD0gt5oZoC1MeG18WtR/7P6ZvdrKTGrfWh7AMCktpPwULuHMKTpEAAoN8UmryQPMd/G4P1/3i/XBr9e6/rVRcs6LeGh9EBCToLTk7N4eIKfG+bgxUTa1GsDf09/qJVqFGoKK51LIMaEfeWNCRdqCnEr75b4t7Nd0laJcHh4uDgFacSIEZg4cSIAoF+/fli/fj02bNiAhx56yHG9rATSC0uuuCYXdi6MADDrnlnwUnnh94u/G1WGAVi8ZszaMRi9ZjR0ep1N+yorwkqFUlyZxZrl5yoivzQfxdpi+Kh9EB7IlkmUZkc3r91c3FYVEQRBLOko/a64uGQVZ4kJRYC880EdDR/43NPwHgDyibC0WEfZesbmEOPBIc2MttuTxcx/i3p+9cq9VlG7ZUXYUt83XdqEjw98XO49mUWZ4trTAJBaULEIC4KAD/Z9gLSCNDy28TGTCZKWYsJdG3ZF0otJWDlmJRQKBUY0HwEA2HzFWISPpRzDidsn8NPpn8q1IYqwb12olWpxoHwt61qF/bdEsbbYpkzthGwWxkrISbD4Pp7s1KZuGygUCsMAuZJGkcmYsAz3p7LXV5UUYXeET0EA5JtLdjP7JgBDBi7AqlnN6MTmrJWtvsPT3fNK88SbmbWUFWEAss4V5hd0Pb964gmdVWxIzOI33apqCU//czoaLmqI9IJ0sY+1fWobzX81EmE3tIR58o0jLGEvtRd81D7Q6rUWk/2uZJgWYXvm8/Lj4SJi1G4FFvbptNMAAAUUSCtIw7n0cybfJwgCHt/0OF7f9Tou3L1g9Jr03gBYN0A7cfsEzqafBcDE56P9H4mv8YpilipmAUB4YLgYPx/UdBDUSjX2J+xHbmmu+B4uAMm5yeWEkWdH1/WrC8CQhMh/o8pw/s55hCwIwdzdc616f6muVLQaLYmwVq/FijMrAAAPtnwQAAwD5EpMU9ILeqPrPMgrCAooZCmtW9aiJxGWCWn2oxwiXFBagPSCdHiqPBEWEGb02uz7ZkOlUGHlmZWiUO+6vgu7buwS33M69bRN+zMlwnJOU5Je0HyEKp2iVNUt4b+v/Y07hXew58YeY0tYMvVGKsKp+akOKagiN3pBL1oKPA4s11xhqSUMWJfZXzYzmmPPmsKWRNhDyRLrtHptud+rVFeK+PR4KKBAn/p9AJh3SafkpYj3AH5Ncvh2fvzWuKOXn2LZ2L0jewNgGdpr49di+Krh8P3IFxPXTRTbMeWOLkuwdzB6RvSETtDh8J3D4nYuACW6knIDYGl2NGAYGFXkFrbEuvPrUKQtsnq6VHJusvi7lP1epfx15S+k5qeiRe0WuK/RfQBglyWcXZwNvaBHkFcQPFQeUClVss1M4YMYtZKlSJEIy4SRO1oGa46P+iKDIstlgzap1QST202GTtDhxe0v4nrWdbyxm1nB9f3rAzB2o1mDo0VYekH7evjCU+WJYm2xKFxVWYRLtCWiZXsk+YjR/EHpaFsqwlq9tkoeS1n4zSbYO1j8DRxhCQOwKi7Mb/Dc6uLYZQlnmxdhhUIhuqTLxoUv3r0IjV6DpiFNRRHecX2HyX2cTDXMCy97U+WWMM/nqEiEi7XFWHWWJUV9MfgLPNrpUWj0GkxcNxFbrmwBwOLFKXlsRoMpd7QphjcfDgA4kG5YjEDa16ScJPG5NIeDW8J8YGSrl03Knpt7AFgfF5cKb0peitlVvr478R0A4LHOj4nZ7/ZYwtJ4MEculzQ/x7s2ZJ6npNwkS2+XnWorwnK7o29k3QBg7IqW8tr9r0GtVOOPi38g+sto/HvrX4T6hWLBA8xFzd1o1mLSHS3jXGGpO1oar5FaKUqFErklubIupycHUnE9euuoUaxIOtqWJlsA7hEXlg4o+LmWkJ0gSzk9uyzhsu5oB1nCgPliIHwg26F+B3Stw26Y/yT8YzKJS1qcpZwI/2cJt6nbBioFWz/bUnGQTZc2Ias4Cx3rd0SH+h3w8QMfo0FAA/h5+GHO/XMQ90QcRrUYBYDFtKXFfCzRK7IXAOBqrsGSlfZVep4XaArEHA4elhLd0ZUU4WJtMQ4nMSu8MiKsF/RGfeSk5KXgryt/Qa1UY1qHaeJ2e0RTeo3L0Z6Uq1ns++/XuB8AsoRlQ+qOliMxy1Q8WEqbem2wf/p+PNTuIdFifbfPu6IrpqpZwqI7+j/XFj+hNXqNuF97EykchfQiOXH7BFLymQVilJglsYRVCpbV6w5xYWmmt7faGw0DGkIn6IysospQqiuFRq+BWqkWLc2KluLMK8lDan4qvFReaBTUyOg1qSV8OOkwfr/wu9V9qVCEzVTN4tdQx9COqOtdF23qtkGhplAUEinWWMJhAWFichjfdjT5aLnvmruip3dk03Hq+NbBxVkXkfZKGj5+4GN0DuuMPyb9gV3TdmHLlC1WT9niIppUmCTGf6VWmPS5NHzELUt73dFHko+InowCTYFR6d3bebdN3mfKuqBNxYV/OvUTdIIOI1uMNEq+s+d+Yup+yK1iuSzhfk1IhGVF7pgwP/maBDcx+557w+/FL2N+QerLqTj55Ek82eVJRNWKgr+nP27n3y63YIIlnOaO9jMWYYCN5v09/WU7yeVGepGU6EqwL2EfgDJTlIqzxJtpu9B2ANzDEpYmnwAGobLXJc2T+fw8/MSbOD+3zFlB/OYUHRJdLgTDhbJYW4wHf30QY9aOEX8HS2QVZSG7OBt+Hn7iALAs5lzd3JvUoT5zIz8Q9QAAYNPlTeXakJZzNGcJh/qFiuGi1PxUXM64jPt+uA9DVg4RRfF23m1sv7YdHkoPTGk3RWwjwCtA9Exx+jXpJ/bJGoK9g1HHtw6KdcW4nX8bgiCYtYSlmdGcRkGN4KH0QEpeSqWSNaVzrgHD9XEz+yaafNEEj218rNxn+H2QD2zLirJOr8P3J78HADzaybhuhC1rWJfF1P2Q37MsGVknbp/A/T/cb9YIKtYWIyknCSqFCt0bdYdKoUJqfqrDy6ZKqbYiLHdM+Ea2ZXe0lACvAHSs3xEAm1rE5wjakpzlzOxowBCvAZgAKBQKq05yV1D2psrdqXV868Bb7Q1PlSdKdaXib9YljBWEcDdLGJBPhLmVw13RgMGSMufONOeKBgxCGZ8eL4ra3N1zK5zqIrWCpZWypJiapiQIgsES/u/amtx2MgAWf+TTgwA2aJRaaGVjfHwAGupvLMJ7b+6FXtAj/k68WEN49bnV0At6DGs+zOhalAtuDV/NvIrMokyjAbY5S5hj6zSlpJwk9PihB9afXw/AEA/mAyx+zzxx+wRKdCX468pf5X5PLrpdGrBrik9X4iw/tRzXsq6hcXBjDIoeZPSadPqgrUjDNJwQb2N39NXMq3hx24tGRsOCgwtwKOkQfjpVfsoXwMKMAgREBkfCW+0tTtdML7beYLKXainCgiDIHhMWLeFa5i1hc3QM7QjA+riwtBay9KST1R1daNodDRgEQO4i6XLBb7B8cMPhgwc+4i7VlcLPww+t6rYC4J6WcHStaAAwWhi9MpgSYZ74Za5tc9OTAIMlLM2q3Z+432yiFKciV7S0bak1civvFjKLMhHiE4KGAQ0BAN3Cu6FnRE/klORg2Yll4nvF2PF/iVfJuclG8/SlljBfvCQ1P1Wc1woAK8+sBGAowhPbvvxSrnIgFeGyg8uKLGFAkpxlxTSl387/hoNJB/HwHw/jwp0LOJJ8BAooxNg0F2HuQcopySkn7vw+2KdxHwDG7uhCbSHm7ZkHAJjff365KoXSqZC2wq8LSzHhzw59hs+Pfi5OHyvVlWLb1W3l+imlbOIhD7ukFsmzqIc1VEsRzi3JNXJlySnC1ljCZeHuM2vjwjklOdAJOgR4Bog3JMAgwpYSszQ6jVVTccq6o6XVi/iJzoWgqokwv1lNaD3BaDsfPEit+vDAcPFG61aW8H/fPZ+mdCT5SKXaO5d+DrkluSZFuEVtVintcsZlk5/lCStlpycBBkuYu6D5dVGRNWyVCJtwR0utYKkFPfu+2QCAz498LiYQ8qSs7uHdUc+vHrR6rZFnjA/QpZbw7fzbRrHlNfFrcDbtLE6mnkSwdzCGNqtcecyKEL0RGVfE85p/N9LYdNnpSZymtaxPzrp49yIAdv8Y+MtAlOpK0bF+R7Sqwwap/DuSDgbiUgzlFvkcYaVCKa6KJBW3FddWIDU/Fd0adsPENhPL7V86FdJWLLmjeXt8wLD63Gro9Do2B/u/xTLMTafiIsx/h4igCABAajGJsF3wkS6/4djrjs4ryUNGUQa81d7icm62wN1nlizhOTvmYPbx2dDqtSZdT4DBHW3OEs4tyUWj/zXCAz8/UGFMg++Du6ONLGEfY0u4qhXs4DeJYc2HiQXkfdQ+4iBFOqAIDwwX53W7hQgXGbuj7w2/FwBwPOW4zXGqs2ln0f7r9nh046MmRZiP/q9lXjOZfW2NJcwttK+GfoVQv1AcSzmG3y+aT9KyRoRNuaO5sHLrljOs+TC0rNMSSblJ+DX+VwDAiVQWD+4U1km8qfJzRhAEkzHh83fO41LGJXirvdEkuAlS81Px5OYnAQDjWo0Tq9XJjWgJZ10V3c/dw7sDMC7YUfZ65fABkjXJWVyElQqlaGX3adzHyCUPGLvBpUv78TnCDQMaiv3m7uhbubfw87WfAQALBy40GWqQY4qSSRH+byUlPoMlJS8F+xP3G+UKVCTC/HgiAv8TYbKE7YOfTHyEl1mUaVehBqkVbC6OZYm29dpCqVDiwp0LJktO6gU9vjj6Bfak7sGFOxdMnnBAxe7oM2lnkFaQhj039+Dl7S+b7Y9e0Jdzb0mFq6wIVyVLWJq8ElUrSrQUpd+VWUvYDd3RIT4haFG7BUp0JTZn2B9POQ4BAv668pf4G0pF2M/TD+GB4dDoNUaxvetZ1/HXlb/ESlNl5wgDBmsVYDf13pG98WbPNwEAD//xMA4mHjTZJ0tzhMW2Tbijudu7W8NuRu9VKpR4pfsrAFj8r0hTJAp257DO5UQ4pyQHpbpSBHgGwMfDRxSgv678BQCICYvB1PZTARjKLvK/HYEpd3Truq0R7B2MEl2JeC8oO0eYU1FcXwoX4UUDF4nb+jbua7UIS++D0u9VL+ix6PAilOhLMLbVWNwfcb/J/cuRHW0UE5bcn3R6nZEFv/LMSiMRzirOEq1iKdzbI4rwf8eVVpRW7r2OolqKMHc3hQeGI8grCHpBb1dVFVuSskzh6+GL5rWbQyfoytWXBtjIjbve4u/EmxVhcZ6wmcQs6Wjv/479H1afXW3yfRsubECxthiNgxuLbZqKCVdFd/Tdwrso0hYh2DsYgV6B4k1ZOonfkiVsS41cV1A2MQsAujdilpGpqTiW4OdtoaZQTMKRijBQPi785dEvEf1lNIatGobMokwEegWiYWDDcm1LRbhdvXbw8/TD0/c8jSntpiC/NB+DVw7GgcQD5T5XGXd0kaZIbItPI5Eytf1UNAxoiHPp59Dv5364lHEJKoUKbeu1FS0bLixSVzQA8dzgJSe7h3c3yoJuFNgIPSN7mu2rvUhFmLt2I4Ii0CiQxSa5xWouJiz9vCUyCjNwp/AO/Dz88Fy35zD7vtno07gP+kf1N4hwQXl39InbJ8RrRirCvh6+qOdXDxq9Bil5KVh7nq0t8HJ384N/MSZcCUu4opjwrbxb0Og1ohdlxZkVuJ51HXV864gDlbJJZEWaInFgW1aEyRK2kWJtMbou64ppv7OJ4VJ3E//R7IkLiydfUONKt8HdaNIiAhxp5mt8unkRrsgS5icZT+Z5bNNj5S5OXogeMMTTAGMR5vutiu5ofoPgFwufh82TdQAYFUsIDwxHgGcAfD18UagpFG+2VZWyljBgcE9KE4esQToo41ZBWREuGxdeG89upveG34unYp7CuvHrTK4XLM1V4C5zlVKFn0f/jIfaPYT80nwMXTnUaACn1WvFc9TSgLasO/pQ0iGU6ErQsX7HcpYg78u2qdvQKLARjiQfgV7Qo3Xd1vBWe4uJNvy8kd4bAENFO073Rt3Rsk5LdA7rDACY0m6Kzesl20KITwiCPIKQX5qPY7eOAWDnNs/S5YMHcyGqiKAIeKo8K5ymxK3glnVaQqFQ4JMBn2DPw3vg6+FrZAlrdBrczrsNpUKJOr51kF2cLd6fyubFRAZFAgB+PfcrknOTEeodKp4LpuAeKrliwtIplLxvncM6o31oe3EAN6zZMESHRBv1nzP/wHykF6SjXb124mCURLiSHE46jGMpx7Dy7Erkl+aLbpVQf4MI2zPNxp7MaA5P6T+YVN5NZyTCUkvYxzYR5v18qftLGNNqDAo1hWK5Pc7my5txOu00wvwNC08A5acoAfJNhpeTsiI8uuVoLBq4CPP7zxffU9YSVigUbuOSNlWer7IizC1hwPC9mbOEL2dcRpGmCP/e+hcKKLD1oa34evjXGBA9wGTbUktYeuNVKVX4afRP6Fi/I/JK84xc6Ek5SdAJOjQMaGgxxlrWHc1rsD/QxPwc3Lb12uLIY0fQqX4nAIYShGXd0dwS5rHVciL833e9cOBCjG01Fi/c+4LZfcpFuB8TXP572WIJq5Qq0atgyRqWinBZpCKckpcCAQLC/MPE75C7pLmlLopwMBPhz49+DgB4oMEDFsN1PmofeKo8UaIrERe9sAadXifeg6TGgtQS5vHgJsFNMKWtwZMxovkI0XiSivDVzKvigjtLhi0RB1pSEXaW16xaiPD+xP0AWKzzxO0T4oVW37++eDOzxxK21x0NAAOi2M3s72t/l/txzYpwWXc0nydsJjtaepE81I4tLcndkACzgt/fx9YpffX+V41uhO4yRYnfTPkoXKlQ4sXuL4oFOYDyMWEAbpGcJQhCuexogMUIAzwDkJiTKNYntgZ+Y5JiSYSPJB+BRq9Bh/odKiy9aMoS5qiUKrSr165cH6xxRQPl3dF8kYb+Uf0tfq5BQAP888g/WDp8Kd7vy87zciJcxhL29/QXr6vIoEjxPOnTuA/WTVhXTqQdQSNfQzUyBRRoGNBQPG+5CJedzSDFGpe0JRHm30VafpooVI2CGiEmjOVb8AzpspYwFzfexwfCLBcqqexyhmUXb+BIK77xzOgmwU0wqe0kKKCAl8oLA6MHiv3l/RcEAc9tfQ6lulI83OFhMdMbAIK8gxDgGYAiXZHdC0NYS7USYQA4duuY49zRdohwu9B2CPULxa28W+WWVpOK8NXMq2LNY1vd0byfkUGR4ty/w0mHxWSwv6/9jWMpx1DPrx6eiHnC6LOmpihVxWId0riZOcpawgDcwhIu1BSiRFcCL5WX+FsDTNS6hbPYt7Vx4RJtCVLyUqBUKMUERcC8O/pSxiUx+YmvFGQJLpTShSak8MpyUmvcWhGWuqOzirIQdzsOHkoP9IyoODYb4BWAJ2KeEMXUnCXMY8KAYYDGY+/OppGfQYTDAsLgofIwckcXagpRqCmEp8rT5ApNPOZpbqoZAFzMMC/CXmov1PKuBZ2gEz0XEUERBhG+bVqEuSXM3982uG2Fx1qZuLCpeDDAipUEeQVBgCCWKW0c3BiRwZH4Y9If2DR5EwK8AsR+3sxh/d9+bTu2Xt2KIK8gsba/FJ5x7qzQlduLsFavNboxHUuRiLB/qGhR2BPXlLo6KotSoRRde9uvbjd6TSrCekEvuqxtEWG9oDdYicGRqONbR4yNHE0+CgBYcnwJAODFe180uskDZtzRVTAxq6w72hT8WLxUXuIxuMNcYen0pLJuPVtd0ok5iRAgoFFgI9ELA5QX4cjgSHgoPZCcm4ytV7cCsFKE/7OEuzXsZjJmym/UlRFh/lt9E/cNtl/bDr2gR/dG3cuVibSGen714KnyREZRBgo1heUsYcDgjuXfsbORijA/r3ksOzk3WUzmDPULNenu5YOs83fLJ31yLty5YPTesvDv4FgKi0s3Cmwkzjw4cfsESnWlSM5NhgIKcYDAvVEAm8ZlzcwRa6tmFWmKEPt7LNadX2fWMwgYRJ2XKeUhw5EtRor327KW8MZLGwGw+6B0MMb5bsR3eKfjO2JIwNG4vQhfyr2EAk2BKCr/3vrXEBOWwRLOLs5GTkkOfD187S5bNzBqIADg7+t/G23nN6fWQa0BGE4Ws9nRJtzR6QXpKNGVoLZPbfFG2yeyDwBWI/Zu4V38deUvKBVKPNLxkXKfVyvVqOdXDyqFSoyXBXoFQqVQIa80r8qspGSVCP9nCfN4MCBxR1dhS9iUK5rDE9CsFWEufk1qNUHfJn3F7WVFWK1Ui4kr/976FwCsygbuVL8T1Eo1xrYaa/J1fjOUxuGsmZ4EAK/c9woigiJwJPkIntjEPDaW4sGWUCqU4s00KSfJqGQlZ0LrCWgW0gxjWo2p1D7sxZQIi5ZwThI+OfgJAJjtX9t6zAI9l37O5OvF2mLcyL4BpUJpcroZYBBhfg40CmyEhgENUc+vHrKKsxD5eSSbIxzYUPRUSC3hCW0mlG/UBNYW7Pjryl/45cwveHLzk+I1L82T4HAR5mEaU4aSdDUywFBgxlx4o1NYJwwPH16p6aiVwe1F+GQmc0OMbz0evh6+uJF9A7dymTvXyBKupEtVunCDvT8KL+7+z81/RBdxQWkB0grS4KnyRLe6xnMgLVnC+aX5GLF6BL6N+9aon1KXOS8tt+fmHqyNXwutXouB0QPNxrnWjF2DX8f9iiDvIAD/xXDsyGh0BGVjwqZoW68t6vnVM6py5E6WsKnBHp+KFZcSZ1VSi9R70zuyNxRg525ZEQYMLmmALe9nzWCzf1R/5L2eh8djHjf5uuiOlsSEubuUZ++bo65fXfw+8Xd4q71Fl6AtCyOUhVuVp1JP4egt5hWSZtM/2+1ZXH72sih8zsZIhAONRfhm9k2sO78OnipPo9kMUlrXZYP3C3cuGJXn5FzNvAq9oEdUrSijWL4Ufk/g840jgiKgUChE70BqfirC/MMwr9c88TNNQ5qinl89dAjtICZxVYT0fvLWnrcw4bcJJufvnkk7I77vf0f+B8CyJQz8N+AKKm+9hvqFwlvtjYyiDCRkJyD+Tjy8VF64p8E9VvXZ0ahd3QF7OZnBRLhP4z64lnUNBxIPQCfo4OfhB39Pf4MlXFQ5S3h/Aos3m4ql2EpYQBjah7bHmbQzOJh4EP2j+otWcJPgJogOML45mRPhgtIC/HnxT2y+vBln0s7giZgnxFGedHTaK7IXFFDgcPJh8WY2tZ35wgNSi4kT6heKu4V3kZKXYtJ1wzl5+yS2XNmCOffPMUqekJNibTHSCtKgVqotJszU9q2NlJdSjGrXukNilqk5wpxaPrVwT4N7cCzlGDZf3ozxbcZbbEs6KKvlUwudwjrhxO0TRvFyjjSma40rmmMpw7lhYEOolWrczr+NIk0RPFQeoku0Tb02FbbdOawzlo1YhtjfYxHsHYx7Glb+hsmty6f/ehqZRZm4N/xecbZCVSDIIwjB3sHILs4W++rv6S9uA4AZHWeYnK8NsGSiRoGNkJSbhGtZ18rF6C0lZXHKXk9czD4f/DkGRA1A90bd0al+JyNDxNfDFxdmXYBaqbbaQOHn3wf7PhCTqbR6LdZNMJ4Kdyb9jPicW+dlZ4sAxtdKwwCDlS5FoVAgMigSlzIuibXAu4V3MzsgcTZubQnrBT1OZZ4CAPSM6Gk0suGCYe8UpdXnWMGLca3H2dFTA6JL+hpzSXMRjg6JRlSAwU2ngMJolAcYl63kWc88Y1aalMWp7Vsb7UPbo1RXihO3T8DPww+jW462qb/cVVnRKi2zd8zGvD3zxExWR8Br6YYHhpcrDl+Wsq9zS5h7SaoipuYIS+EZ77+c/aXCtkR39H8W6dfDvsajzR4VvSNSpDdtntBnL2qlWnQDJ+Yk4krGFZToShAZFIlAr0Cr2pjafir+nPQntkzZArWy8vYCty4zizIR4BmAVWNWVXj+OBOFQlFuAQEA4venUqgwp8cci23wgU18eny510QRrm29CPPBQOPgxpjVdRY6h3U2KbQhPiFW/578/QC7n6gUKgR4BuD3i79jwQHjBCluCfN7HmDGEvY23CMtTSHlHsIVZ1YAAHpFyHOey4Fbi/DFuxeRo8lBmH8YompFGblE+EllzxSlG1k3cDj5MHw9fDGi+QhZ+jyoKVvei8eFxWSV4ChE+kWKo8EQn5ByNwqpO1o69ehw0uFyc/g40pvu2NZjbU5u4QXiK6rIczb9LADjRBy5sSYebI7okGioFCpczbxaqaUglx5fatVSffZgKSYMAJPaToJKocJfV/6q8HyWxoQBNm92ZouZJsXHyBJubL0lXBF83zeyb4jnh3QqmTWMbDFSjIdXFun58s3wb+ya7+8onu/2PAZEDTCqCMZd0rEdYiucmdG2rvm4MJ+NwVcTM4VUhL1UXmbXerYXqSfm+5HfY9VYVsfgzd1vYsc1tgJXXkkermddh6fKUyyFCliOCQOWE2f598crwzmyCpqtuLUIc1dxz8ieUCgUxpawn7ElfLfwLgRBwOg1o9Hnxz4mYydlWXNuDQBgVItRlcrMNEWPiB7w9fDFqdRTuJ513Shj1EvlJY6ITY36eB+yirOMMqoPJx82aQkDxiJcmeXYrJmDmFGYISa8SFd+kRtujVdmqpivhy/ahbaDTtAZLfhuDaW6Ujy37Tl8uP/DctPL5IT/hmWL9HNC/UMxIHoAtHqtWNnKHLZk9LcPbY8QnxDc1+g+WefFSuPCZ9P+E+F6tomwHPSM7AkvlRdmdplpVJKyKjG1/VT8Hfu3kVU5s8tMDIgagHf7vFvh50VL+I4FS9hKd7Q0oVFu+kf1R1StKHwx+As83PFhDG8+HPN6zYMAAR/u/xCAYSDRum5rzLxnppjHUFFM2BoRBphnwVWZ8KZwaxHmN0Qex4qqFSX+KFyEpQUnTqaexJ+X/sQ/Cf+YrOFcFu6K5ouHy4G32lvMcvzp1E/lMkbb1GUXk6kTzkvlJSbYAIZR5ZHkI6IlLI0JA+y7CfIKQrOQZujbuHzMtyKsEWGpMCXmJpp9n73wGzn/jmylawPmKeHJOdZyJe+KWL2JT/eSG0EQsPMGc+VbGqXzmD6PbZmioLQAdwrvwFPlKcbCLRHsHYyLsy5i60Nbbey1ZaTTlM7dYTdWV4hwyzotkfNaDpYMW+L0fdvDiBYj8Hfs31Z5fsxlSCdkJ+B06mmolWoxgcsUPFwDVM7TZC1t67XFteeu4bluz4nbXur+ElQKFQ4mHURuSa7oim4f2h7B3sH4uP/H6Fi/o8lQiVSELQ3OpcZJp7BOCPAqP9/aVbi1CD99z9N4vNnjmN5xOgAYWcM8Juyp8kSgVyB0gk7MJAYM8+HMcS79HM6mn0Ut71qiC1kueH9/Ov2TKG7WiLBCoTCa3/ts12cBsNVyzBUUqeVTC2dmnsHBGQcrFQezRoSlAxp7LeEd13bg59M/m1xaz94bOS94YasIx2cbrAtbP2stlzMuIzEnEXV864hLX5pidMvR8PPww+Hkw7iWaRyn5yuFSb0i1tY9rutX16bYnjVwy+Rm9k2DJWyjO1ouqkoSjqPg838vZ1w2mk64+N/F0Ak6TGgzwWIVNKklbCrD2JEEewfj3vB7odVrsev6LoMI12sPAJjVdRZOPnmyYkvYipgwULXiwYAVIqzX6/HWW29h4sSJiI2NRUKC8UoUH3zwAcaMGYPY2FjExsYiL895BfKb126OJ1s8CR8PH3EbX0xa6oblMTYelAeYcJlDEAR8f+J7ACwhy1TGnT30adwHjYMbIyEnQZy2wU8gbgWZG7VK3eJjWo1ByzotUaIrQaGmEIFegSYvtIigCJPl7qyhUVAjeCg9cCvvltlKXTzrFTBeBs1WMosyMXz1cDz8x8Po/n138WIE2G9i742cT/Ph2ZbWci7LYF04SoR3XGfxsAeiHrAonH6efniw1YMAgJVnV4rbT6eeRsD8ALy287Vy8WBXwfd/Lv0crmddh4fSw2g6FCEffp5+iKoVBY1eI04zyivJw7ITywCwwhSWqO1bGyoFG6TzRDZnMqTpEADA1qtbxczo9qHtK/xcZdzRciUfykWFIrxz506Ulpbi119/xcsvv4yPP/7Y6PX4+Hh89913WLFiBVasWIGAANea+Y90fAQlc0uMRJiPoAo1heINzpQlrBf0WBu/FjHfxohFyXlGqpwoFUo83OFh8e96fvXEuMeAqAG4OOsi3uv7nsnPcku4tk9ttAttZ1S3156ymuZQK9XizVQah5YirdRzK/eWVfF2U6w7v050+x5POY6Yb2Ow+fJmAGyeYkZRBoK8gozmeNpCyzotxRrMvKCLNUgt4bNpZ80ORuyBZ8vz7HlL8HNyzbk1YqLY0rilKNQUYsHBBfj6+NcA7Fv1Sw74TfHC3QsQIKBlnZYOm75GGLxo3CW9/NRy5JbkomdEzwqnZCkVStF76GxLGACGNJOIcJrtIuyh9ECDgAZm3xfqH4pg72B4KD3MrnfsKioU4bi4OPTsyayzjh074tw5g1Wg1+uRkJCAt956C5MmTcK6desc11MrUSgU5SxXqRtjesfpUECB06mnxaXSBEHApkub0PGbjpi4biJOpp5EqF8oFg9ZLGu2qBSpCEsrCCkUCrSo08LslAwuwr0b94ZSoTRKMLBUwMIeKnJJc0tYqVBCo9eIpQFtha/49OXgLzGtwzRo9Vp8dewrADDKrq1s0ohKqRLnm1ob280uzkZCQQI8VZ5oXbc1dIJOLGgvFxqdRsx2N7dqkZT+Tfqjtk9tXLh7AefSz0Gr12L9hfXi63yBeldbwqH+ocbrDrvIFV1T4HHh+PR46PQ6fH7kcwAVW8Ec7pJ2VrlGKR3rd0SoXyiSc5ORW5KLen71LNYl4EQGR6Kub130iOhhMdymVCixefJmbH1oq92VD+Wmwsl3+fn58Pc3VNlRqVTQarVQq9UoLCzE1KlTMX36dOh0OkybNg1t27ZFy5aWC1vExcl7E6uwPUmBoZ6+PbHLfxdu5t/Er//8ijbBbfDOqXewOZlZXPV96uORpo9gRPgIeKm8ZO+rlC61u+B4xnHUEmqJ+6lwf8xQRLQyGnFxcQjKDRJf8in1cUh/AzUsVrj3zF40KjC+QPM1+UjKTYKn0hORfpG4kncFO47uQNtabW3qS2pRKvYl7IOn0hPthfZoXrc5fsbP2HtjL44cO4KtN1nSUH1FfbuOMVLFBip/xv2J8IKKKyQdvcPEunlAczT3aY7zOI/1R9fD965vBZ+0TGpRKvam7sXw8OG4knsF+aX5aOLfBGlX0pCGigcxver2wu+Jv+PznZ+jS50uSC9IRyPfRqjvUx/HMpiXR8gSyn1XjjyfTVHfuz4SClgIK0QTIuv+nX0sjkSOY/HNZ+fkrou7cC35Gm5k30BD34ZoWNDQqvaH1h0KtUYN/0z/SvfHnuPoUqsLthRsAQA09mlsdVvre62Hp9Kzwvd7//fP2naddX5VKML+/v4oKDDMq9Tr9VCr2cd8fHwwbdo0+PiwmOy9996LixcvVijCMTEx9vTZiLi4uArba3GnBbbe2oomwU0wrf807MrbhZtnbiI/MB9+kX7YvHkzvNXemN9/Pp7q8pTFSkBy8n7Q+xi5eiSmdJ2CmPYxVh3LsMxhSD+VjmcHPItGQY3QUd8RAUcCkFeah67Nusr63XLu096HNTfXoNi3uFz7PL7asm5LNAlugiuXrsCvgR9QZNvv/NmhzyBAwMiWI9H7XuZ9aHWqFS7cvQBtqBbZKdkAgH5t+tl1jCP9RmL51eVI1CWabEej0+DF7cxy+HLIl9h+gC220bd5X3Sq3wkbEjfgluKW3d/z2LVjseHCBuzK2CUmE45sM9LqdmfVmoXfV/yOfZn7oApkFsC0mGl4qstTaP91e2QVZ2HIPUOMkrysOb/kptXFVki4ykR4cKfBiGkuz/5dcSyOQq5jUTdUY96peTiYfhAH09kiMB8P+hhd21tXUjImJgbv4/1K79/e45jqNRVbkpkI92jaw6W/r9znlyVBr1CEO3fujD179mDo0KE4deoUmjc3TOy/efMmXnjhBfzxxx/Q6/U4ceIEHnzwQXl6LSMt6rBkkIc7PCxmUK84swLHUo6JLtap7aY6ZQFvKYObDkbRm0U2ZS1/MuATfPzAx2JsW6VU4b5G92H7te3iElxyY8kdLV2dhU/wT8pJQhNP21yh3BUtXZC7f5P+uHD3AnZd3yUmZXGXW2XhyVnHUo5BL+iNkqAEQcCTm5/E8lPLAbAsbJ6I1bVhV3QO6wzA/mlKRZoibLu6DQBwMvWkuAzbwOiK48Gc3o17o55fPVzNvCpmpE9sMxHhgeHY+8henEo9ZTHL2llI49L2/naEZVrUaQEPpQc0eg1a122Nb4d/W+Xin5YYGD0QSoUSekFvVTy4ulChCA8YMAAHDx7EpEmTIAgCPvroIyxfvhwRERHo378/Ro0ahQkTJsDDwwOjRo1Cs2aOEQJ7mNFpBpqFNBOTtXhc8GDiQbHy0FNdnnJJ3yozbahs9uziIYux9epWDG8+XK5uGVFWhDMKM3Ar7xbah7YXpye1rtta9CAk5SYBZsIugiAgoyjDKC5z4c4FnEw9iSCvIDFBA2AT+//v2P/h7+t/i0UI7L2RhwWEiXV2L969aJSF/v6+97H81HKolWpo9VrM2TlH/H26NeyG6JBoBHoFIik3CSl5KRYTQQDDWs6NgxujUVAjMc6/8/pOFGoK0aZuG3irvcX1cm2p26xWqjGu1TgsOb4EJboStKrTSvxu2oe2rzI3MR6XDvQKdOj8U4LVIPhx9I/ILMrEEzFPyD6rw9GE+IRgQNQA7Lqxy60GD/ZSoQgrlUq8955xpm50tGGhgcceewyPPfaY/D2TEU+Vp9GyVR1CO0CtVIup/F0adBHXznRHmtVu5jArGGDJDyqFCok5iSjUFOKBFQ/gVOoprBu/zlASr04rcW5vYk5iOREWBAE7ru/Am7vfxPGU43jmnmewaNAi5Jbk4rFN7PwZ02qMUSigdyRLPjuUdAgAq+QjXfe4snQL74ak80lYenwpvhjyBQBgWdwyvL33bSgVSqyfsB5L45aKCU4BHgFoGtIUCoUCXRt2xc7rO3E0+ag4VcgU/9z8B31+6iP+3SCgAf6e+jfa1GuDPy7+AYCVoXy+2/N45e9X0LJOS5ursk1sO1FcI3pim4lOW3rNFnjSYdt6batk/6obVbUimLWsGbcG6QXpZpdcrI64/SpKlcHHwwdt67XFqdRTAICnYlxjBbsLnipPRAZH4nrWdby15y3xe5uxcYaY/dq6bmtxxZeyc4U1Og3GrB0jTjcCgP879n84nXYaaQVpuJxxGY0CG2Fur7lGn6vlUwudwzqLc7rlqrb0XNfn8MfFP/Dlv18iqlYUfDx88OTmJwGwzOyRLUaifWh7tP6qNYq0RWgT3EYUkG4Nu2Hn9Z3Yfm27KMJZRVlYeHghHmz5oDiY+/TQpwCYFyGnOAcpeSl4+e+XsWXKFmy6vAkAK7wR4BWApSOWVuo4ekT0QGRQJJJykzCx7US7vhNHMbz5cDwZ86Q4f58gLBHsHWyxqEh1xK0rZtkDT4YJ9ArEpLaTXNybqk+zEGZpLzy8EABbgza3JBd3Cu9ApVChWe1m4vzCslWzfr/4OzZf3owgryAseGABdk/bjTD/MOxP3I/LGZfRPrQ9Dj962ORi7/0aGwrayxVT7BnZEz+M/AEA8OL2F0UBXjhwIWZ1nQWAzbme338+AOD+ugbX2KgWo6BUKLE0bil+OfMLcopzMOiXQfhw/4cYtmoYsoqycOnuJWy5sgVeKi8cmnEI52edR6BXILZf2473/nkPdwrvILpWdKXLb3KUCiV2xO7Avkf2ybLUpiPwVnvjm+HfmFwmkyCIGizCg6JZKconY56UbXGG6ozUPdStYTccfeyouK1pSFN4qjzRIKABlAolUvNTodEbSuf937//BwD4qP9HePX+V9G3SV/EPRGHEc1HYGKbidg/fb/ZtVKlYQQ56w7HdojF/P7zIYAVu1g0cBFe6v6S0Xuev/d53Hz+JiY2MVhx9zS8B18MZi7sGX/OQO8fe4uFX9IK0vDaztfwxVH2+rQO01DXry7q+NbB6z1eBwC8t4+Fdka1GCWLe7ZZ7WY1Kn5GENWNGumOBlj88fRTp+22RmoKUhFeNGgRgryDsG78OgxfPVz0JKiVajQIaIDk3GSkF7NVlU6nnsb+xP0I9ArEtA7TxDbCAsKwcfLGCvfbI6KHmPEpd7GHOffPQX3/+gj2Dja7znJkcCTuKoyXDXym6zO4mnkVXxz9AqfTTiMiKALfDPsGo9aMwrcnvhUTYqTZ9s93ex7/9+//4VYeW8/Y1nWdCYKontRYEVYoFFUmg9QduL8Rs7Zi28eK67t2qN8BiS8kGll0jQIbITk3GWlFrODE4n8XAwAe6fCIWJrTFnw9fPFBvw9w6e4l2VfgUSgUeKTjI5X67MKBC5FXkodTaaewdtxaRIdEY879c/DB/g9QqivF4KaDjTKvfTx88H7f9zFj4wzU8a1j9xq5BEFUD2qsCBO2cU/De5D4QmK5aTllXaqNghrhcPJhpBWlIaMwQ1xkgMdaK8Or979a6c86CpVShe9HfW+07c1eb2Lt+bW4nHEZr3R/pdxnpnWYhvSCdHSo36FSU9MIgqh+kAgTVmNNYXdedzatOA3/O/I/FGuLMbjpYDSv3byCT7o/3mpv7H14Ly5nXDZZc1ylVGFOjzku6BlBEFUVEmFCVnhBhvUJ63H74m0AwMvdX3Zll5xKWEAYwgLCKn4jQRAEanB2NOEYuCV8u4gJ8NfDvsYDUQ+4sksEQRBVFrKECVnhZQoVUOC7kd9hRqcZLu4RQRBE1YVEmJCVDqEdML//fPjk+pAAEwRBVAC5owlZUSgUeK3Ha+gR2sPVXSEIgqjykAgTBEEQhIsgESYIgiAIF0EiTBAEQRAugkSYIAiCIFyEQhAEwZk7jIuLc+buCIIgCMLlxMTEmNzudBEmCIIgCIJB7miCIAiCcBEkwgRBEAThIkiECYIgCMJFkAgTBEEQhIsgESYIgiAIF+G2IqzX6/HWW29h4sSJiI2NRUJCgqu7ZBMajQazZ8/GlClTMG7cOOzatQsJCQmYPHkypkyZgrfffht6vd7V3bSajIwM9O7dG9euXXPr41i6dCkmTpyIMWPG4LfffnPbY9FoNHj55ZcxadIkTJkyxW1/l9OnTyM2NhYAzPb///7v/zBu3DhMmjQJZ86ccWV3zSI9jgsXLmDKlCmIjY3Fo48+irt37wIA1q5dizFjxmDChAnYs2ePK7trEemxcDZt2oSJEyeKf7vjsWRkZGDmzJl46KGHMGnSJCQmJgJwwrEIbsr27duFOXPmCIIgCCdPnhSeeuopF/fINtatWyd88MEHgiAIQlZWltC7d2/hySefFI4cOSIIgiDMmzdP+Pvvv13ZRaspLS0Vnn76aWHgwIHC1atX3fY4jhw5Ijz55JOCTqcT8vPzhS+//NJtj2XHjh3Cc889JwiCIBw4cEB45pln3O5Yvv32W2H48OHC+PHjBUEQTPb/3LlzQmxsrKDX64Vbt24JY8aMcWWXTVL2OB566CHh/PnzgiAIwurVq4WPPvpISE9PF4YPHy6UlJQIubm54vOqRtljEQRBiI+PF6ZNmyZuc9djmTNnjrBlyxZBEATh8OHDwp49e5xyLG5rCcfFxaFnz54AgI4dO+LcuXMu7pFtDB48GM8//zwAQBAEqFQqxMfHo2vXrgCAXr164dChQ67sotUsWLAAkyZNQr169QDAbY/jwIEDaN68OWbNmoWnnnoKffr0cdtjadKkCXQ6HfR6PfLz86FWq93uWCIiIrB48WLxb1P9j4uLQ48ePaBQKNCgQQPodDpkZma6qssmKXscixYtQqtWrQAAOp0OXl5eOHPmDDp16gRPT08EBAQgIiICFy9edFWXzVL2WLKysrBo0SK88cYb4jZ3PZYTJ04gLS0NjzzyCDZt2oSuXbs65VjcVoTz8/Ph7+8v/q1SqaDVal3YI9vw8/ODv78/8vPz8dxzz+GFF16AIAhQKBTi63l5eS7uZcVs2LABISEh4oAIgFseB8BuKOfOncMXX3yBd999F6+88orbHouvry9u3bqFIUOGYN68eYiNjXW7Yxk0aBDUasOS56b6X/Y+UBWPq+xx8MHqiRMn8Msvv+CRRx5Bfn4+AgICxPf4+fkhPz/f6X2tCOmx6HQ6vPnmm3j99dfh5+cnvscdjwUAbt26hcDAQPz4448ICwvDsmXLnHIsbivC/v7+KCgoEP/W6/VGX6g7cPv2bUybNg2jRo3CiBEjoFQafo6CggIEBga6sHfWsX79ehw6dAixsbG4cOEC5syZY2SJuMtxAEBwcDB69OgBT09PREVFwcvLy+iG7k7H8uOPP6JHjx7Yvn07/vzzT7z22mvQaDTi6+50LBxT10fZ+0BBQYHRTbOq8tdff+Htt9/Gt99+i5CQELc8jvj4eCQkJOCdd97BSy+9hKtXr+LDDz90y2MB2PXfr18/AEC/fv1w7tw5pxyL24pw586dsW/fPgDAqVOn0Lx5cxf3yDbu3r2LGTNmYPbs2Rg3bhwAoHXr1jh69CgAYN++fejSpYsru2gVK1euxC+//IIVK1agVatWWLBgAXr16uV2xwGw2q779++HIAhIS0tDUVERunfv7pbHEhgYKN4sgoKCoNVq3fL8kmKq/507d8aBAweg1+uRkpICvV6PkJAQF/fUMn/++ad4zTRq1AgA0L59e8TFxaGkpAR5eXm4du1alb+ntW/fHlu2bMGKFSuwaNEiNG3aFG+++aZbHgvArv9//vkHAHDs2DE0bdrUKcfiXqajhAEDBuDgwYOYNGkSBEHARx995Oou2cQ333yD3NxcLFmyBEuWLAEAvPnmm/jggw+waNEiREVFYdCgQS7uZeWYM2cO5s2b53bH0bdvXxw7dgzjxo2DIAh46623EB4e7pbH8sgjj+CNN97AlClToNFo8OKLL6Jt27ZueSwcU+eVSqVCly5dMHHiRHHGRFVGp9Phww8/RFhYGJ599lkAwD333IPnnnsOsbGxmDJlCgRBwIsvvggvLy8X97Zy1K1b1y2PZc6cOZg7dy7WrFkDf39/LFy4EEFBQQ4/FlrAgSAIgiBchNu6owmCIAjC3SERJgiCIAgXQSJMEARBEC6CRJggCIIgXASJMEEQBEG4CBJhgiAIgnARJMIEQRAE4SJIhAmCIAjCRfw/ctqxFCNHSQUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#from canna import ABuSymbolPd\n",
    "#tsla_df=ABuSymbolPd.make_kl_df('usTSLA',n_folds=2)\n",
    "#import pandas.io.data as web\n",
    "import tushare as ts\n",
    "import json\n",
    "\n",
    "def obj_to_Json(obj):\n",
    "    data = json.dumps(obj, indent=4,ensure_ascii=False)\n",
    "    fh = open('data.json', 'w')\n",
    "    fh.write(data)\n",
    "    fh.close()  # 最终写入的json文件格式:\n",
    "\n",
    "#print(ts.__version__)\n",
    "#ts.set_token('dfeb543866c1b2b3e804f3cbed560e9e1709b0d06cd8182e1cc5676d')\n",
    "pro = ts.pro_api('ea3263c5424f08c3e04d605af9458cfc349613d5b7c27e99994eb396')\n",
    "#ts_pro.get_hist_data('601318') \n",
    "start='20210701'\n",
    "end='20220301'\n",
    "# 中国平安 代替 Tesla at p78\n",
    "zgpa_df = pro.daily(ts_code='601318.SH', start_date=start, end_date=end)\n",
    "#print(zgpa_df.head())\n",
    "#print(type(zgpa_df))\n",
    "import matplotlib.pyplot as plt\n",
    "print(type(zgpa_df['trade_date']))\n",
    "zgpa_df[['close','vol']].plot(subplots=True, style=['r','g'],grid=True)\n",
    "zgpa_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7edf666d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:xlabel='trade_date'>,\n",
       "       <AxesSubplot:xlabel='trade_date'>], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#print(zgpa_df['trade_date'].tolist())\n",
    "x=zgpa_df['trade_date'].tolist()\n",
    "zgpa_df[['trade_date','close','vol']].plot(x='trade_date',subplots=True, style=['r','g'],grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "586f523f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "4  601318.SH   20220223  53.10  53.30  52.69  52.89      53.07   -0.18   \n",
      "3  601318.SH   20220224  52.15  52.25  51.09  51.46      52.89   -1.43   \n",
      "2  601318.SH   20220225  51.70  52.06  51.21  51.40      51.46   -0.06   \n",
      "1  601318.SH   20220228  51.10  51.10  50.25  50.76      51.40   -0.64   \n",
      "0  601318.SH   20220301  51.01  51.40  50.57  51.39      50.76    0.63   \n",
      "\n",
      "   pct_chg        vol      amount  \n",
      "4  -0.3392  463601.66  2.4498e+06  \n",
      "3  -2.7037  975396.02  5.0392e+06  \n",
      "2  -0.1166  544416.39  2.8081e+06  \n",
      "1  -1.2451  731883.26  3.7079e+06  \n",
      "0   1.2411  568035.79  2.8952e+06  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>161.0000</td>\n",
       "      <td>161.0000</td>\n",
       "      <td>161.0000</td>\n",
       "      <td>161.0000</td>\n",
       "      <td>161.0000</td>\n",
       "      <td>161.0000</td>\n",
       "      <td>161.0000</td>\n",
       "      <td>1.6100e+02</td>\n",
       "      <td>1.6100e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>52.4416</td>\n",
       "      <td>53.0591</td>\n",
       "      <td>51.8557</td>\n",
       "      <td>52.4325</td>\n",
       "      <td>52.5072</td>\n",
       "      <td>-0.0747</td>\n",
       "      <td>-0.1130</td>\n",
       "      <td>7.3685e+05</td>\n",
       "      <td>3.8755e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.3536</td>\n",
       "      <td>3.3730</td>\n",
       "      <td>3.1902</td>\n",
       "      <td>3.2715</td>\n",
       "      <td>3.3988</td>\n",
       "      <td>0.9479</td>\n",
       "      <td>1.7936</td>\n",
       "      <td>3.1193e+05</td>\n",
       "      <td>1.6711e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>47.5000</td>\n",
       "      <td>48.0800</td>\n",
       "      <td>47.3000</td>\n",
       "      <td>47.7800</td>\n",
       "      <td>47.7800</td>\n",
       "      <td>-3.1400</td>\n",
       "      <td>-5.4476</td>\n",
       "      <td>2.7470e+05</td>\n",
       "      <td>1.3735e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.2900</td>\n",
       "      <td>50.9400</td>\n",
       "      <td>49.8100</td>\n",
       "      <td>50.2800</td>\n",
       "      <td>50.2800</td>\n",
       "      <td>-0.5600</td>\n",
       "      <td>-1.1270</td>\n",
       "      <td>5.2317e+05</td>\n",
       "      <td>2.7236e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>51.5000</td>\n",
       "      <td>52.1500</td>\n",
       "      <td>51.0600</td>\n",
       "      <td>51.4900</td>\n",
       "      <td>51.5500</td>\n",
       "      <td>-0.1500</td>\n",
       "      <td>-0.2950</td>\n",
       "      <td>6.6980e+05</td>\n",
       "      <td>3.5581e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>53.5000</td>\n",
       "      <td>54.1800</td>\n",
       "      <td>52.8800</td>\n",
       "      <td>53.6500</td>\n",
       "      <td>53.6500</td>\n",
       "      <td>0.4400</td>\n",
       "      <td>0.7887</td>\n",
       "      <td>8.8043e+05</td>\n",
       "      <td>4.6293e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>64.2600</td>\n",
       "      <td>65.1700</td>\n",
       "      <td>63.5800</td>\n",
       "      <td>64.7600</td>\n",
       "      <td>64.7600</td>\n",
       "      <td>3.7400</td>\n",
       "      <td>7.7337</td>\n",
       "      <td>2.1800e+06</td>\n",
       "      <td>1.1319e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           open      high       low     close  pre_close    change   pct_chg  \\\n",
       "count  161.0000  161.0000  161.0000  161.0000   161.0000  161.0000  161.0000   \n",
       "mean    52.4416   53.0591   51.8557   52.4325    52.5072   -0.0747   -0.1130   \n",
       "std      3.3536    3.3730    3.1902    3.2715     3.3988    0.9479    1.7936   \n",
       "min     47.5000   48.0800   47.3000   47.7800    47.7800   -3.1400   -5.4476   \n",
       "25%     50.2900   50.9400   49.8100   50.2800    50.2800   -0.5600   -1.1270   \n",
       "50%     51.5000   52.1500   51.0600   51.4900    51.5500   -0.1500   -0.2950   \n",
       "75%     53.5000   54.1800   52.8800   53.6500    53.6500    0.4400    0.7887   \n",
       "max     64.2600   65.1700   63.5800   64.7600    64.7600    3.7400    7.7337   \n",
       "\n",
       "              vol      amount  \n",
       "count  1.6100e+02  1.6100e+02  \n",
       "mean   7.3685e+05  3.8755e+06  \n",
       "std    3.1193e+05  1.6711e+06  \n",
       "min    2.7470e+05  1.3735e+06  \n",
       "25%    5.2317e+05  2.7236e+06  \n",
       "50%    6.6980e+05  3.5581e+06  \n",
       "75%    8.8043e+05  4.6293e+06  \n",
       "max    2.1800e+06  1.1319e+07  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "# 对中国平安数据进行逆序\n",
    "zgpa_df = zgpa_df.sort_index(axis=0, ascending=False)\n",
    "print(zgpa_df.tail())\n",
    "# x='trade_date', 将trade_date列作为X轴标签\n",
    "zgpa_df[['trade_date','open','close','vol']].plot(x='trade_date',subplots=True, \n",
    "                                           style=['r','b','g'],grid=True, xlabel='Date')\n",
    "zgpa_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "2a72b010",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:xlabel='Date'>, <AxesSubplot:xlabel='Date'>,\n",
       "       <AxesSubplot:xlabel='Date'>], dtype=object)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "#font = FontProperties(fname='/System/Library/Fonts/Supplemental/Songti.ttf')\n",
    "pd.set_option('float_format', '{:f}'.format)\n",
    "zgpa_df[['trade_date','open','close','vol']].plot(x='trade_date',subplots=True, \n",
    "                                           style=['r','b','g'],grid=True, xlabel='Date', title=zgpa_df['ts_code'][0])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "af4e00e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gsccewxtvvIH09HQcPny4zbEffvghZs6ciTfffBNRUVFYtWqVVe6EMzMmN3vz99o5EiIiIiKi/qPLRGffvn2YMGECvL29IZPJ8NBDD8HFxQWBgYHw8/ODVCrFhRdeiN27d5sdp9PpcPLkSYwbNw4AMHXqVOzZs8c698KJeSm9MMhnEPbl77N3KERERERE/UaXQ9fy8/Mhk8mwbNkylJeXIykpCSEhIdBqtaZ9tFotSktLzY6rqqqCq6srpFIpAMDT0xMlJSWWjX6AmBQ2Cd8e+xZ6UQ+JwGlVRERERERd6fJTc3NzM1JSUrBo0SIsW7YMaWlpKCwsbLOfIAhmP4ui2PZiEn5I740Lwy5ERWMFTpWdsncoRERERET9QpeZh1arRWJiItRqNRQKBUaPHo2UlBRUVFSY9ikvL4enp6fZcWq1GnV1ddDr9QCAsrKyNvtQ90wKmwTAsvN0SutLUVpX2vWORERERET9UJeJTlJSEo4ePYqamhro9XocPnwYY8eORW5uLvLz86HX67Fjxw6MGDHC7DiZTIaEhATs2rULALBt2zYMHz7cKnfC2UVoIxCgCrDYPJ1dubsw8ZuJuOWHWyxyPiIiIiIiR9PlHJ3Y2FhcfvnlePrpp6HT6TBs2DDMnDkTwcHBeO2119DY2IgRI0aYmg68++67GDVqFEaNGoU77rgD//3vf/H999/Dx8cHDz74oNXvkDMSBAFj/Mdgb/7edocE9sSatDV4eOvDaNQ3Ymf2Toii2GbYIRERERFRf9etdXSmT5+O6dOnm21LTEzE8uXL2+z7f//3f6Z/+/r64tlnn+1bhATAsJ7OT2d/wvnq8whGcI+PF0URy3cux2O/P4bxgeMxPXQ6lu1bhqyqLISrw60QMRERERGR/bA7QD9hXE+nN8PXRFHE07ufxmO/PYa5UXPx5SVfYlKQYd5PcnGyReMkIiIiInIETHT6iQTPBKgV6jYNCZr1zVh5YiUKa9p2wjM6UHgAHx//GPePvh8rpq+Ai9QF8V7xkEvkOFZ8zNqhExERERHZXLeGrpH9SSVSjPIfhf35+822P7vnWXx8/GNIXCW4KfKmdo89XXYaAPD3CX+HpNaQ27pIXTDUbygrOkRERETklFjR6UfGBIzB6fLTKKk1LLz60bGP8PHxjwEAqcWpHR6XVp4GpVSJME2Y2faRgSORUpzS5wYHRERERESOholOPzLGfwwAYGf2Tvya+Sue2f0MLom4BOMCxuFk8ckOj0svT0ekJhISwfzPnRSYhLKGMuRU51g1biIiIiIiW2Oi049c4HsBFBIF3j3wLu7dci8u8L0Ab017C7GesUgtTu2wMpNekY5oTXSb7SMDRwIAUopTrBo3EREREZGtMdHpR5QyJS7wvQAb0jbAW+mNT2Z+AleZK2K1sSivL0dRXVGbYxqaG5BVlYUYbUyb24b5D4NUkHKeDhERERE5HSY6/czUkKnQKrVYOWsl/Nz8AMCUxKSVp7XZP7MyE3pRj2ht24qOq9wVcZ5xOFbCzmtERERE5FyY6PQzD4x4ANkPZyPeK960rbNEJ708HQDaHboGAIk+iUguTmZDAiIiIiJyKkx0+hmJIIG7wt1sW5AqCCq5qt1EJ63CsK2jRGeYzzAU1xUjvzbf8sESEREREdkJEx0nIAgCEnwSOqzoBLgFtEmOjIb6DAXAhgRERERE5FyY6DiJBJ8EnCk/02Z7ekU6ojRRHR43xGsIJIKEiQ4RERERORUmOk5ikM8g5NbkoqapxrRNFEWkl6e323HNyE3uhhhNDDuvEREREZFTYaLjJBJ8EgD81XwAAErqS1DRWNFux7WWEn0ScayYndeIiIiIyHkw0XESg3wHAfir+QDQdcc1o0SfROTX5iO/mg0JiIiIiMg5MNFxEjFeMZAKUpwp+2uejjHp6WzoGmDovAYAh/IOWS9AIiIiIiIbYqLjJBRSBcLV4W0qOkqpEsHuwZ0eO8R7CAQIOJh70NphEhERERHZBBMdJxKjjUFaWYtEpyIdkZpISITO/8zuCndEaaJwKJ8VHSIiIiJyDkx0nEisNhbnKs9Bp9cBANLK07qcn2OU6JPIig4REREROQ0mOk4kWhuNJn0TMisz0djciOyq7C47rhkl+iQiuzIbxXXFVo6SiIiIiMj6mOg4kVhtLADDkLX00nQ0i83druiMCxwHANicvdlq8RERERER2QoTHSdi7K52puwMTpWcMtvWlQt8LkCENgI/n/3ZavEREREREdkKEx0nolao4e/mj7SKNKQWpwIAojRR3TpWEARcO/habDu/DWX1ZdYMk4iIiIjI6pjoOJloTTTOlBsqOv5u/vBQeHT72GuHXAudqMMvmb9YMUIiIiIiIutjouNkYj1jkV6ejtTi1G7PzzEaGTgS4R7hWHt2rZWiIyIiIiKyDSY6TiZGE4PKxkocyjvU7Y5rRoIgYG7UXGzP2Y7S+tIu92/WN+ORrY9gV/au3oZLRERERGQVTHScTIynoflAY3Njjys6ADA3ai6axWZszNjY5b77C/bjm9Pf4Jtj3/T4OkRERERE1sREx8nEaP7qstbdjmstDfEeggh1RLeGrxk7tBk7vBEREREROQomOk4mUBUIlVwFAL2q6AiCgMujLsfO3J0oqSvpcD+9qMf6jPUAmOgQERERkeNhouNkBEFAjCYGLlIXBLsH9+ocxuFrGzI2dLjPgYIDKKgtQKw2FpnlmajT1fU2ZCIiIiIii2Oi44QuDL4Q0yOnQyqR9ur4QV6DEK2J7nT42s9nf4aL1AV3Jd4FESIyKjN6GS0RERERkeUx0XFCT4x5AutvXN/r443d13bl7UJhTWGb2/WiHuvOrcO0kGkY5jMMAJBent7r6xERERERWRoTHWrX3Ki50It6rDqxqs1tBwsOIr82H3Oi5iBKEwUASCtPs3WIREREREQdYqJD7Yr3jMcFPhdg6dalKKotMrvt53OGYWsXh10MN7kbQtQhSK9gRYeIiIiIHAcTHWqXIAh4fcrrqGyoxCPbHoEoigD+GrY2JWQKPBQeAIB473icrThrz3CJiIiIiMww0aEOJXgl4NWLX8WW7C34+PjHAIC95/ciryYPl0VeZtov3jse6eXppmSIiIiIiMjemOhQp+4dfS8uCrsIy/Ytw4mSE1h1YhUUEgUuDr/YtE+8TzyqmqpQVFfUyZmIiIiIiGyHiQ51ShAEvD75dWgUGty35T58d+I7TA6ZDLVCbdon3jseADhPh4iIiIgcBhMd6pK3qzfenPomTpefRnZlttmwNQBI8EkAwBbTREREROQ4mOhQt0wJmYJ7h90LtYsaM8Nnmt0WqgmFUqpkRYeIiIiIHAYTHeq2J8c8ifMPn4fGRWO2XSJIEKmJZEWHiIiIiBwGEx3qNkEQ4OHi0e5t0ZpoVnSIiIiIyGEw0SGLiNZGI7sqG43NjfYOhYiIiIgIsu7stHTpUlRUVEAqlQIAJk6ciJ07d5puLywsxOTJk3H77bebHbdq1Sps2bIFKpUKADBjxgzMnj3bUrGTA4nWRKNZbEZmZSYiEGHvcIiIiIhogOsy0RFFEbm5uVixYoUp0QGAefPmAQCys7OxfPlyXHPNNW2OTUtLw0MPPYS4uDjLRUwOKVobDcDQYnoKptg5GiIiIiIa6LpMdHJzcwEAL774IiorK9tUZT788ENcf/31UKvVbY49e/Ys1qxZg8LCQgwaNAg333wzFAqFBcMnRxGt+TPRYUMCIiIiInIAXc7RqampQWJiIhYvXowlS5Zg06ZNSE5OBgAkJyejsbER48ePb3NcfX09IiMjccstt+Dll19GTU0Nvv/+e8vfA3IIHgoP+Ln6sSEBERERETmELhOduLg43H///VAqlVCr1Zg2bRoOHToEAPjtt98wZ86cdo9TKpV44oknEBAQAKlUirlz5+Lw4cOWjZ4cSrSWndeIiIiIyDF0meikpqYiJSXFbJtMJoNOp8OJEycwatSodo8rLi7Gli1bTD+Lomg2x4ecT5QmikPXiIiIiMghdGvo2hdffIHGxkbU1dVh69atGDNmDDIzMxEYGAilUtnucQqFAl9++SUKCwshiiJ++eUXjBkzxuJ3gBxHtCYaZQ1lKKktsXcoRERERDTAddmMICkpCWfOnMHjjz8OvV6PWbNmIS4uDrt27YK3t3eb/V966SVce+21iI6Oxp133omXX34ZOp0O8fHxmDt3rlXuBDkGY+e1UyWnECGNsG8wRERERDSgdWsdnQULFmDBggVm2yZMmIAJEya02feJJ54w/XvcuHEYN25cH0Ok/sLYee1U8SlE+EfYNxgiIiIiGtC6HLpG1F2hHqGQS+Q4VXLK3qEQERER0QDHRIcsRiaRIUIdwUSHiIiIiOyOiQ5ZVIw2BqeKmegQERERkX0x0SGLCvUIRVZFlr3DICIiIqIBjokOWZS30hs1TTWo09XZOxQiIiIiGsCY6JBFeSsNLcdL6riWDhERERHZDxMdsihv1z8TnXomOkRERERkP0x0yKJMFR0mOkRERERkR0x0yKJMFR0OXSMiIiIiO2KiQxbFig4REREROQImOmRR7nJ3KKQKVnSIiIiIyK6Y6JBFCYIAP5UfKzpEREREZFdMdMjifN18megQERERkV0x0SGL81X5cugaEREREdkVEx2yOFZ0iIiIiMjemOiQxXGODhERERHZGxMdsjhfN1/UNNWgTldn71CIiIiIaIBiokMW56vyBQCU1pfaORIiIiIiGqiY6JDF+boZEh02JCAiIiIie2GiQxbnp/IDAM7TISIiIiK7YaJDFmccusZEh4iIiIjshYkOWZxx6FpxXbGdIyEiIiKigYqJDlmc2kUNuUTOZgREREREZDdMdMjiBEGAt6s3mxEQERERkd0w0SGr8FZ6o7ieQ9eIiIiIyD6Y6JBVeCu92YyAiIiIiOyGiQ5ZhbfSG6V1nKNDRERERPbBRIeswtuVFR0iIiIish8mOmQV3kpvVDdVo15Xb+9QiIiIiGgAYqJDVuHt6g2Ai4YSERERkX3I7B0AOSdvpSHRKa0vRbB7sJ2j6ZviumK8sO4FlFWWmW2/b+J9iJJF2SkqIiIiIuoMEx2yClNFxwnW0vkh7Qe8c+AdBKgCIEAAYEh+GiQNePvCt+0cHRERERG1h4kOWYWxouMMa+nszd+LSG0kdlyzw7Tttl9vw5nSM3aMioiIiIg6wzk6ZBXGRKe/V3T0oh578vZgcvhks+1RmiicKTkDvai36PXWnVuH9w68Z9FzEhEREQ1ETHTIKtQKNeQSOUrr+/daOmfKzqCsoQxTwqeYbY/SRKGhuQG51bkWvd4HKR/g1d2vWvScRERERAMREx2yCkEQ4K3s/2vp7M7fDQDtVnQA4GzFWYteL7MyE4U1hRY9JxEREdFAxESHrMZL6YXiuv49R2dv3l4EqAIQ5WneXc0aiU5tUy0K6wpR2VDJ9YeIiIiI+oiJDlmNt2v/ruiIoog9eXswLmAcBEEwu83X1RceCg+LJjqZVZmmf/fn3xsRERGRI2CiQ1bjrfTu13N0zlacRWFdIcYFjmtzmyAIiPOOs2yiU9ki0ennTRyIiIiI7I2JDlmNt6t3v/7Avjd/LwBgfOD4dm+3dKKTUZlh+ndRXZHFzktEREQ0EDHRIavxVnqjqqkKDc0N9g6lV3bn7YaPqw+iNdHt3h7rFYvs6myL3b+WFR1nWH+IiIiIyJ6Y6JDV9Pe1dPbm78XYgLFt5ucYxXnHQS/qkVWZZZHrZVZmIkYbA6D//s6IiIiIHAUTHbIaY6LTH+fpZJRnIKc6B+MC2s7PMYrzjgNguc5rmVWZGOw1GG5yt37frY6IiIjI3mTd2Wnp0qWoqKiAVCoFANx111346quv2myLjY01Oy45ORkrV65EY2MjJkyYgAULFlg4fHJkPq4+ANAvP7Rvy9wGAO02IjCK9TY83i2R6DTpm5BdlY25UXPhp/Lrl78zIiIiIkfSZaIjiiJyc3OxYsUKU1LT3rbWGhsb8c4772Dp0qXw9vbGv/71Lxw+fBgjRoyw7D0gh+Wl9ALQP1slb83YCq2LFgleCR3uo1Vq4ePqY5FEJ6c6B81iMyLUEUx0iIiIiCygy0QnNzcXAPDiiy+isrISM2bMQGJiYptts2fPNjsuLS0NgYGB8PPzAwBceOGF2L17NxOdAcTb9c85Ov0w0dmWtQ1jAsZAInQ+ujNKHWWRRMfYiCBcHQ4/lR/OlZzr8zmJiIjIsXx/5ns0ZzXj2rBr7R3KgNBlolNTU4PExETccccdaGxsxNKlS1FdXd1mW1BQEIYNG2Y6rrS0FFqt1vSzVqtFaWn/m6tBvadRaCATZP0u0cmvyUdaaRquj72+y32jNFHYkr2lz9c0tpYO9wiHv8ofB84f6PM5iYiIyHHoRT1e2v8SIICJjo10mejExcUhLs4w6VqpVGLatGkoLi7G/fffb7bt0KFDZolOezrqXkXOSRCEfrmWTlfr57QUpYnC16e/RlVjVZ+umVmZCRepCwJUAaaha6Io8jlDRETkJI4UHUFeTR4AoKapBiq5ys4ROb8uu66lpqYiJSXFbFtmZmabbTKZec7k5eWFiooK08/l5eXw9PTsS6zUD3kpvfpdRWd33m54KDwwxHtIl/tGaaIAAOcq+jbULLMyE2EeYZAIEvip/KATdahorOj6QCIiIuoX1p1bZ/p3Xz83UPd0mejU1NTgiy++QGNjI+rq6rB161YMGTKkzbYxY8aYHRcTE4Pc3Fzk5+dDr9djx44dnJ8zAHkr+19FJ7koGWOCx0Am6bopoTHR6es8ncyqTISrwwEAfirDvDY2JCAiInIOoihi/bn1CHEPAQCkV6TbOaKBoctPcklJSThz5gwef/xx6PV6zJo1C5deeil0Op3ZNuPwtsWLF+OJJ56Al5cX7r33Xrz22mtobGzEiBEjMG5cx616yTn5uPrgSNERe4fRI5lVmRgf3vWwNcDQPECA0KdERxRFZFZmYkLQBADmiY5xAVEiIiLqv46VHENWVRaeH/88luxeYrE1+Khz3VpHZ8GCBW3WwGlvGwAsX77c9O/ExESzn2ng6W8VncrGSpQ3lCPSM7Jb+ytlSoS4h3TrBUun16G6sbrN9oKaAtTqahHhEQEA8Ff5A2BFh4iIyFmsO7cOUkGKeTHz8P7x9zl0zUa6HLpG1BdeSi9UNVWhQddg71C6JasqCwAQqe1eogMYhq91J9F55cArGPTfQdDpdWbb00sN5es2Q9fqmeiQ4/vm1DeY/+18e4dBROSwRFHEunPrMD5wPLyUXoj1jsXZSlZ0bIGJDlmVcS2d4tr+8aE9uzIbABDlGdXtY4yJjiiKHe4jiiJ+SPsB5yvPY0/eHrPb0svMEx1vN28IEPpVJYwGrp25O7H65GrU6ersHQoRkUM6XnQcZyvO4tLISwEAcV5xrOjYCBMdsiofpQ8AoKi2yM6RdI+potPNoWuAIdGpaqpCYU1hh/scKzmG3BrD4rsbMjaY3ZZemg4BAkI9QgEAMokMnkpPFNX1j98ZDWzG7oDGRW+JiMjc9ye+hwABl0RcAgCI845DeUM5Suu5vqS1MdEhqzJWdDpLAhxJVlUWPOQe8FR2vxW6sfPa6ZLTHe6zMWMjJIIEk8MnY0PGBuhFvem29LJ0BLkHwUXqYtrmo/RhRYf6hfKGcgBslUpE1JFVJ1dhTMAY+LkZhqbHescC6HvHVuoaEx2yKm+lIdEpqC6wcyTdk1WVhTB1WI8W6uxOovNL5i8Y4z8Gd428CwW1BThUeMh0W3pZOsI9ws3293H1YTMC6hdMiU4lEx0iotbSy9NxrPAY5kTOMW2L8zZ0KmaiY31MdMiqgtyDAPw198XRZVdlI8wjrEfHBLsHQyFRdJjoZFZm4mTpScyKmIXL4i6DXCI3G76WXpqOCHWE2TE+rj5sRkD9QkWDYegaKzpERG2tz1gPAKZha4Ch4ZFUkPJ10waY6JBVucpc4aX0Qma544/fF0WxV4mOVCJFhDoCp0vbT3R+yfwFADArfBY0Sg0uDL4QG85tgCiKqG6sRlFtkakRgZGPK4eukeMTRdFU0eE3k0REba07tw5jg8eavvgFALlUjlCPUL5u2gATHbK6EPcQZFVmWf06oiiisbmx18cX1hWivrm+x4kOYBi+1lFF55eMXzDIa5Apmbkk4hJkVmXiROkJZFRlAECbio630hsVjRVoaO4fbblpYKrT1aFJ3wSAQ9eIiFrLq8lDSnEK5g9q24K/u0tTUN8w0SGrC3YPtklF5/VDryPizYheJzvGjmvG7mc9EaWJQlppGup19Wbbi2uLsa9gH2aFzzJtmxk+ExJBgg0ZG0ydqtobugaAVR1yaGUNZQAM7dgLagtQ01Rj54iIiBxHSnEKAGBi2MQ2t0VponCu8lynS1NQ3zHRIasLdg9GVkWWVZ/MRbVFeCf5HeRV5+Fg4cFencNYdWo9jKw7poRMQWNzI5buWWq2/efTP0Mv6jE7YrZpm4+rD8YGjMX6c+tNiU7ra/q6+gIASuqZ6JDjMg5bGxM8BgCrOkRELZ0oOQEASPRLbHNblCYKdbo65Nfm2zqsAYWJDlldiHsIappqTN/+WsN/j/4XDc0NkAgSbDu/rVfnMFZ0QtxDenzspOBJWDxhMVaeXInVaatN29ekrkGQKghDvYea7X9JxCU4VXYKW7K3wMfNBx4KD7PbTQutsvMaOTBjI4KxwWMBsCEBEVFLJ0pPIEIdAQ8Xjza3RWoM6/Vx+Jp1MdEhqwt2DwYA5FbnWuX8eTV5WHlyJa6OvRrjQsZhW07vEx1/N38oZcpeHf/ijBcxLmAcHtv+GE6VnkKdrg6/pv+K2RGz27SrNlZ4duftRrRndJtzGYeucdFQcmStKzp8wyYi+suJkhMY7DW43duiNYb3fr5uWhcTHbI6Y4XkfPV5q5z/rSNvoVnfjIdHPIyZUTNxtOhor1Yb7k3HtZZkEhlWzFgBd7k77vztTqw7tw51ujqz+TlGwe7BGOE7AgAQ7dVOoqPkHB1yfMaKTqg6FAFuAazoEBH9qbapFhmVGRjs3X6iE6gKhFKq7Nbr5s7cnYh9KxYFtR2vSfjt6W8x4r0RbGLUChMdsjpjRccaiU5meSa+Sv0K18VfhzB1GC6OvhgiROzM3dnjc2VVZfWqEUFL/m7+WDF9Bc5VnsPibYuhVWoxNnBsu/sae+q3V9FRyVVQSpVcS4ccmrGi4+nqiUhNJOfoEBH96WTpSYgQO6zoSAQJItQR3arofHr8U6SVpuHzk5+3e3uzvhlvHnoTR/KP4Les3/oUt7NhokNW56X0gqvMFTnVORY/9wvbXoAAAQ+OeBCAYQiNh9yjx/N0GpsbkVud26tGBK1NCJqAf4z6Bxr1jaYFQtszJ2oOFBIFRgSMaHObIAiGRUM5R4ccWHlDOeQSOVRyFSLVkazoEBH96USpoRFBRxUdoHstpssbyvFb1m+QCBJ8fvLzdis2m7M3I7MqE1JBiu9Of9e3wJ0MEx2yOkEQEKYJs3hFJ6MyA58c+QQ3JtxoqhrJJDJMCp6EbTnbetTlLasiCyLEPld0jO654B4sGbsET056ssN9ItQROHjjQcxLmNfu7Vw0lBxdeUM5tC5aCIKASE0kSupLTMPZiIgGshMlJ6BWqDttcBSliUJWVRZ0el2H+6w/tx6N+kYsm74MxXXF+Cn9pzb7fHz8YwSqAvHg2Afxe/bv/JK0BSY6ZBPh2nCLNyN449AbkEvlWDRikdn2ycGTcb76fI8m+J0rM3wT3Zc5Oi1JBAn+b9j/YZDvoE7381J6tWlUYOSt9GYzAnJo5Q3l0LhoAACRakMHIQ5fIyIyVHQGeQ3q8D0eMHRea9I3dfpF8Oq01YjWROPxiY8jVhuLj49/bPZF7umy09iesx1/G/w33D7yduhEHX5I+8Gi96U/Y6JDNhGuCbdoRedYyTF8f+Z73Df6Pvi7+ZvdNjlkMgD0qPva2TJDUmSpRMcSfFx9OEeHHFpFYwW0LloAhm8mAbaYJiLSi/pOO64ZGV83O/piNqc6B7vzduOqmKsgCAJuG3IbkouTcaDwgGmfj49/DBepC25MuBGDfQfjAp8L8N0ZDl8zYqJDNhGmCUNxXTHqdHV9Ppcoinh297PQumjx1IVPtbk9Qh2BcI/wHiU658rPQS6RI8AtoM/xWYpx6BpXTSZHVd5QDo3CUNEJV4dDgMBEh4gGvMzKTNTqajudnwN0negYKzNXxlwJALg69mqoFWp8fOxjAEBZXRlWnVmFK6OvhJfSCwBwTdw1OF5y3LRY6UDHRIdsIlxjmORvieFrGzI2YHfebjw66lF4unq2u8/kkMnYmbsTTfqmbp3zXPk5BLsHQyqR9jk+S/Fx9UGTvgmVjZX2DoWoXeX15aaKjlKmRJB7EIeuEdGA151GBIBhiLpaoW73CyJRFLE6bTVG+Y8yNUpyk7vh+vjrse7cOuRW5+Ljwx+jTleH24beZjruiugrIJfIWdX5ExMdsokwjWFIWF87r9Xr6vH83ueR4JmAmxJu6nC/KcFTUNNUg0MFh7p13rNlZxHu0feOa5ZkXDSUkwrJUVU0VkCr1Jp+Zuc1IiJDIwKJIEG8Z3yn+wmCgEh1ZLsVneSCZJwqO4WrYq4y275wyEKIEPHpiU/x9v63MT5wPIZ4DzHd7qX0wkVhF+GHtB86bXIwUDDRIZsI1xqSiPYSnbL6Mty//n7Tmhyd+fDYh8iqysIz45+BTCLrcL8JQRMgFaTdHr52ruycxTquWYpx0VAmOuSImvXNqGyshFahNW2L1BjesDnckogGshOlJxCtiYarzLXLfTtqMf1F8heQCTLMjZprtj3UIxQzw2bi3eR3kVGegduG3Nbm2Gtir0FRXRH+OP9Hr++Ds2CiQzYR7BEMiSBptyHBxoyN+O/+/3a4EJZRXlUe/nPkP5gZPhOTgyd3uq/GRYPhvsOxNWdrl7FVNVahpK7EoRoRAIC3qzcAsCEBOaSKRkMbaePQNcDwhl3RWIGyhjI7RUVEZH8nSk50OWzNKEoThZzqHNTr6k3bmvXN+OrYV5geNt0096al24behmaxGWGaMMwMn9nm9mmh0+Cl9OKaOmCiQzYil8rh7+bfbqJzoMDQPeTzk5+jWd/c4Tn+ueWfaGxuxJKxS7p1zSkhU3C06CjK6jr/0JVVlQUACFM7VqLDoWvkyIwVWGN7aeCvFtM9ae1ORNRbVY1VSCtP6/Szg62V15fjfPX5LjuuGUVqIiFCxOu7X8fJ0pPQi3rsytuF3KpcXBl9ZbvHTAicgKtirsKy6cvaHd2ikCowL3oefs38tcvPQM6u47E/RBYW4h7S7tC1A4UHoFVqkVOdgy3ZW3Bx+MVt9kkpTsEnRz7B3cPuNnUp6crkkMl4/dDruPvnuzEraBYmBU+Ch8KjzX7ZVdkAHKu1NGCYpAiAi4aSQzImOi0rOpGaP9fS4TwdIrKC02Wn8cS+J3Ai/wQyKjNMXwSuuHQFrgi+ws7RGSQXJAPouhGB0Wj/0QhSBeGpLYYusmqFGu5yd3goPNr9PAQY5va8Ne0tBAUFITe3/SZP18Reg4+Pf4zPkz/H1aFX9+KeOAdWdMhmgt2D2yQ6pfWlSCtPw8PjHoa/mz9WnlzZ5jhRFPH0rqfh4+aDB0c82O3rjfQdiRvib8CGtA2447c7MHTlUFy19ip8duQzs/1MFR0HS3RkEhk8XTw5dI0cUkWDYehay4pOmEcYJIKEndeIyCr+d+p/+CL5CygkCswMm4knRz+JALcAbD632d6hmRzNPwoA3a7ohHiEYN/1+5D+QDrenPIm5kbNhVqhxiPjH+nWHJ+OJPokYoz/GLy882WLLO3RX7GiQzYT4h6CdefWmZWYDxUauqJNCZ+CisoKvHHoDWRUZiBCHWHaZ+3ZtdhXsA/vXfYe1Ap1t68nlUixfPJyfHLNJ1h7ZC3+OP8Hfsv8Dbf+eCvenPImrom7BgCQVZkFtYva7JtpR+Hj6oOiuiJ7h0HURnsVHYVUgVD3UFZ0iMgq8mryEO0Zje8u+2vuSWpZKnZm74QoihAEwY7RGRwtOAovpVebxcw7IwgCojyjcE3cNabPJp1Va7p7zkdHPYpr112LL05+gTsT7+z1ufozVnTIZoLcg9Ckb0JhXaFp24GCA5AKUowOHo0bEm6ARJDgi5NfmG6v09Vh2b5lGOw1GLePuL1X11VIFRgfOB5PjH4CG67cgOmR07F4+2Lsyt0FwFDRidRGOsQLZGvGRUOJHI2xouPpYr6WVUcdhIiI+iq/Jh/B6mCzbaP9RyO/Oh+ZVZl2isrc0YKjGOw12CE+U0wMmohpEdPw9tG3UdtUa+9w7IKJDtlMiHsIAJg1JDhQcABDvYfCTe6GQFUgZkXMwv9O/c9UZn0/5X2crz6PpeOXWmQxT4VUge+v/R4R6gjcsekOpJWnIbsqG1Ge3Zv3Y2veSm82IyCHZOyspnYxr7JGaiJxrvIcW0wTkcXl1eQhRB1itm1MwBgAwP78/fYIyYxOr0NKQUq35+fYwvPTnkdxXTE+PfGpvUOxCyY6ZDPB7oZvYXKrDaVYnV6HI0VHMMp/lGmfvw36G8obyvHz2Z+RW5WLt4+8jUsjLsWEoAkWi0Or1GLlrJWQS+W4eePNpoqOI/J19UVJPSs65HjKG8rhLneHXCI32x6pjkRNUw0KagrsFBkROSO9qEdBbQFCPMwTnTjPOGiVWuwvsH2ik1+Tj4r6CtPPZyvOoqG5odvzc2xhYthETAuZhhVHV6C6sdre4dgc5+iQzRgTnfNVhorOydKTqNPVIck/ybTPxKCJiNZE47OTn+FQ+SHo9Dr8c+w/LR5LmDoMn8z8BNf8fA3qm+sR6emYiY63qzfKG8rR2Nxo71CIzFQ0VJg1IjAydl57ecfLkOvkqNXVoqapBnOGzMEEreW+sCCigaWkrgRN+qY2Q9ckggQTQifYvKLT2NyIqd9NRa2uFkl+SZgSMgXNomEOsiNVdADg0VGPYs6aOfjw2Id4JeIVe4djU6zokM14KDygUWiQU2PovGZcP6dlRUcQBPxt8N9wuPAwVh5dibsS70K4Otwq8Yz0G4m3pr0FuUSOkYEjrXKNvjKupVNUw4YE5FjKG8rbbeAx2HswFBIF3tz7JpYfXI4Pj32IL1O/xOJNi20fJBH1SWFtIRb9vgi7snfZOxTk1+YDQJuhawAwMXQiTpefRlm97daMyarKQlVTFWZGz0R9cz2WH1yO1w+9DrlEjlhtrM3i6I7hvsMxM3wm3k95H+X15fYOx6aY6JBNBbsHmyo6BwoOIFAVaKr0GF0dezVcZa7wV/lj0fBFVo3n0shLcerWU5gQ6pjfNPsoDYnOscJjdo6EyFxFQ0W7iY6/mz+Sb05G2eNlyLg9A2dvO4uHRjyEjPIM1DTV2D5QIuqVYyXHMGfNHKxOW41n/njG3uEgryYPABDsEdzmtklhkwD89QWqLRi7Sy6ZvAQbrtyAozcdxVvT3sI3V38DhVRhszi669GkR1HRWIHXd79u71BsiokO2VSIRwhyawxzdA4UHDCr5hhpXDRYMX0Fvr/2e7gr3K0ek4vUxerX6K1hvsPgLnfH7C9n47Zfb7PpizhRZ8obytsdugYYqrdapdY0fyfBKwEAcKrslM3iI6Le25ixEfN+mgcRIuZFz8Pms5vbXfDbloyfHdqr6IwOGg25RG7TeTrG9cJivQ3VGx9XH1wVcxWuHHSlzWLoiSHeQzA7fDbeOfDOgGoWw0SHbCpYZajo5FTm4Hz1+XYTHQCYGT4TE8Mm2jg6xxPsHoxd1+3CkslLsDd/L6746QpctfYqHMk/Yu/QaIArbyhv01q6I/Ge8QCAU6VMdIgcmSiKeHnHy7hj0x1I8EzAunnr8PioxyFCxHenv+v6BFaUX5MPqSCFn8qvzW2uclck+iTadJ7O2Yqz0Cg08Hb1ttk1+2pG2AwU1xYPqCUAmOiQTYV4hKCqqQob0jYAQIeJDv3F29Ubz017Dvuu34dnxz2LtPI03LX2LnuHRQOYKIqoaKyARtF+Rae1cHU4XGWuOFl20sqREVFfrDqzCv/Y/A/MjZqL7y77Dv5u/ghTh2FaxDR8d+Y7u1YC8mry4O/m3+FSE6P9R+No8VE0NDfYJJ5zFecQpYlyiPVyusv4metA4cAZHcJEh2wqSBUEAPj62NdQSpUY4j3EzhH1Hyq5Cncm3onbh96O/bn7UVDL9r1kH3W6OjQ0N7Q7R6c9EkGCIX5DWNEhcnCHCg+Zho+7ylxN228dfisyKjOwL3+f3WLLr8lHgCqgw9vHBIxBQ3MDkouTbRLPucpzpi6T/UWMNgZapRYHCw7aOxSbYaJDNmXsf/97xu8Y7ju8zRoc1LWZ4TMBAL9l/WbnSGigKq0rBWBYk6q7Ev0SOUeHyMFlVGYg1ju2TZVi/qD5cJe745vT39gpMkNFJ1AV2OHtxmqFLYav1evqkVudi0h1/0p0JIIE40PGD6j5vkx0yKaMHdb0op7D1nopwTMBEdoI/Jr5q71DoQGqrM7QwrW7Q9cAYKjfUBTVFaGkjgvgEjmqcxXnEOvVtjWySqHC3Ki5WHt2rV26J4qiiLyavE4rOj6uPojSRNmkIUFmZSZEiP2uogMAE0In4FTZKVQ0VHS9sxNgokM25evqC4XE0Hax5UKh1H2CIGBu3FzsyNmBOl2dvcOhAchU0enm0DXAkOgAQGpZqjVCIqI+amhuQE5NTruJDgBcG3ctanW1WHdunY0jA6qaqlCrqzUNf+/IGP8x2J+/3+pziYyT+ftrogMYhikOBEx0yKYkggRB7oYXKlZ0eu/y+MtR31yP7Tnb7R0KDUDGRfl6kugk+iUCAFJLmegQOaLsqmzoRT1ivGLavX20/2hEqCPw7elvbRyZYX4OAAS4dVzRAYDRAaNR1lCGUyXWHSZrbC3d34auAcCY4DGQCJIBM3yNiQ7ZXLhHOOK94+Gl9LJ3KP3W5PDJ8JB7YFPmJnuHQgNQbyo6Ae4B0LpoOU+HyEEZqxTGdWFaEwQB18Zdi915u3G2zLbtiY2LhXY2RwcwJGMAsCNrh1XjOVdxDt5K7w7XEnNk7gp3DPYaPGASHVl3dlq6dCkqKioglRpa+t111104cuQIdu/eDQAYOXIkbrrppjbHrVq1Clu2bIFKpQIAzJgxA7Nnz7ZU7NRPPT/heWi9tUCzvSPpvxRSBaaGTsWmrE3Qi3p7h0MDjDHR6cmbvCAIGOQ1iBUdIgd1ruLPBTC9YtFQ3n6L5qtjr8byA8vx2ZHPcHf83TaLzVjR6SrRidJEwVvpjR1ZO3BpwKVWi6c/dlxraZT/KHx35js065s7bNftLLpMdERRRG5uLlasWGFKdJKTk5GcnIxXXnkFAPDiiy9i3759GDNmjNmxaWlpeOihhxAXF2eF0Km/itZGI8g/CLm5ufYOpV+7OOxirD27FkeLjiIkuO1K0UTWUlZXBqkghbvcvUfHxXvGY9WZVQNqVW6i/iKjMgMahQZerl7IK89rd59g92BMDp6MV3a9gq3pWzE6YDRG+4/GSL+RVo0tt8bwecFf5d/pfoIgYLT/aOzM3gmM6XTXPjlXcQ4XBl9ovQtY2Sj/Ufj0xKdILUt1+mU+ukx0jB9GX3zxRVRWVmLGjBkYMmQIbr75ZshkhsODg4NRXFzc5tizZ89izZo1KCwsxKBBg3DzzTdDoVBY+C4QDUzTQ6dDKkjxa+avmDN8jr3DoQGktK4UWhdtjxfKi/eMR3VTNXKqcxCMYCtFR0S9YaxSdPW8/tekf2Fl+kr8cfYPvHbwNYgQIZfI8fqs13FVyFVWiS2/Jh/eSm+4SF263Hd0wGhszNyIotoi+Lr5WjyWmqYa5Nfm9/uKDgAcKDjg9IlOl3N0ampqkJiYiMWLF2PJkiXYtGkTysrKTFWavLw87Nq1CyNGjDA7rr6+HpGRkbjlllvw8ssvo6amBt9//7117gXRAOSp9MSYgDHYlMV5OmRbZfVlvRqbPshrEAB2XiNyROcqznVrcn2YOgwr5qzAb/N/w/FbjuOL2V9gcvBkLNqwCK8ffN0qFduuWku3ZJynY60208ZGBFGaKKuc3xZC3EPg7+Y/IObpdJnoxMXF4f7774dSqYRarca0adNw6JChJV12djZeeOEF3HzzzQgMNB83qVQq8cQTTyAgIABSqRRz587F4cOHrXMviAaoi8IuwsnSk8gsz7R3KDSAGCs6PRXnafiCjPN0iBxLQ3MDcqpzelyl0LhoMC10Gj6e+TFuHX4rXjv0Gp7Z/YzF547m1+Z3OT/HKNEnEUqZEvvy91k0BiPjXKb+XNERBAFJ/kk4WHDQ3qFYXZeJTmpqKlJSUsy2yWQypKam4rnnnsMNN9yAqVOntjmuuLgYW7ZsMf0siqJpjg8RWcbM8JkAgLWn19o5EhpIepvoaFw0CFIFsfMakYPJqsyCCBER6oheHS+TyPDR5R/hjqF34KPjH+HhrQ9Dp9dZLL68mrwuW0sbKaQKjAkeY7VqhSnR6YetpVsa5TcKmVWZKKwttHcoVtWtoWtffPEFGhsbUVdXh61btyIpKQnLly/Hgw8+iIkTJ7Z7nEKhwJdffonCwkKIoohffvmlTbMCIuqbKE0UojXR+OnUT/YOhQaQsvqyXiU6AJDglYCTpSctGxAR9YlpXZg+VCkkggTPjnsWi5MWY9WZVbhv3X0Wia1eV4/S+tJuV3QAYGLoRKQUp1hlUe1zlefg7+YPlVxl8XPbknGejrNXdbpMdJKSkjBixAg8/vjj+Mc//oFp06Zhz549aGpqwmeffYbFixdj8eLF+PXXXwEAL730EtLT06FWq3HnnXfi5ZdfxkMPPQRRFDF37lyr3yGigWZm+Ez8kfEHSutL7R0KDRC9regAQIJnAtLL09HU3GTZoIio1yxVpRAEAQ+NfAjzoudhzak1FogMyK0yNMXqSaIzKWwSdKIOhwstP2XibMXZfl/NAYChPkPhInXBgULnnqfTrXV0FixYgAULFphtW7hwYbv7PvHEE6Z/jxs3DuPGjetDeETUlatjr8Z7Ke/hzcNv4rnxz9k7HHJyzfpmVNRX9HqhvHiveDTqG5FWmgYN+t9ie0TO6FzlOWhdtPBUelrkfMN9h2NN+hoU1xXDx9WnT+c6X3keQM8SnfEh4wEA+/L3YULQhD5dv7VzFecwK3yWRc9pDy5SFwzzGeb0DQm6rOgQkWNL8ErA7SNux2fHP0N6ebq9wyEnV9lYCRFinyo6AJBSmNLFnkRkK93tuNZd8V7xACzTeCSnMgcAut11DQA8XT2R4Jlg8Q/xFfUVKKkv6deNCFpK8k9CclEyGnTtLxDrDJjoEDmB56c9D6VMiRf2vWDvUMjJlTeUAwA0it5VY2K0MZAIEhwrPGbBqIioLzIqMyz64d34hYYlGo/0pqIDGOagHCg4gGZ9c59jMDpTegZA/29EYDTKbxQa9Y04kOu8VR0mOkROwN/dH4uGL8Kvmb9iR84Oe4dDFnSo8BBu+/E2fHr8UxwvOW72pp1bnYsf03/EP3f+E6/tes0m8VQ0VgBArys6SpkSkepIVnSIHES9rh451Tm97rjWHl9XX/i4+VimolOVA3e5OzwUHj06bkzAGFQ1VVm0y+PpktMA+ndr6ZbGB42Hq8wV7x18z96hWE235ugQkeO7Y+gd+OLkF3h2z7P45cpf7B0OWcjXp77Gl6lfmn72kHtgqM9Q5NTmIKsiCwAgFaRoPtGMxCsTMdRnqFXjKa8vBwBoldpenyPBK4EVHSIHcbbsLESIFv3wLggChvoNtcjiwOcrz/do2JqRceHQfQX7MNh7cJ/jAIAzJYaKTrg63CLnszetixY3JdyEj1M+xr2D7kWYOszeIVkcKzpETkIpU+LJMU/iZOlJfHP6mx4fv79gP1YeXWmFyLp2uPAw3tzzpl2u7egyKzMxJngM9izYg/9M/Q/mxcxDna4OY4PH4rnxz2HjlRtx5KYj8HL1wov7XrR6PKaKjkLb63MkeCYgvTTdKq1fiah9Dc0N+DH1R4iiaLY9rTQNgOWHYyX6JeJU2ak+Lx6aU5XT42FrABDqEYoAtwAcyLfcsKwzpWcQ7B4MV5mrxc5pb3cPuxsSQYJ3kt+xdyhWwUSHyInMjZqL0f6j8cqBV1DVUNWjY18/+Doe2viQdQLrRGNzI+7//X48/MvDyKjMsPn1HV1WVRaiPaMR6hGK+bHz8a9J/8K6eevw7TXf4vahtyPRJxFeSi88deFT2JqzFdtztls1nrKGMgC9H7oGGCYqixBxuuy0haIioq6sOrMK876Zh01Zm8y2G6sUlh6ONdRvKGqaapBTndOn85yvPN/txUJbEgQBo/xHYV/Bvj5dv6UzpWecZn6OUaAqELcOvxXfnP4GBbUF9g7H4pjoEDkRQRDw7PhnUVRXhJd3vtzt45r1zThUeAhl9WWobqy2YoRtfXriU1OC80PaDza9tqNr0jchpzoHUZ5RXe577+h7EewejBf3vdjnb1A7Yxy61tv20sBfE5UtMayFiLpnd+5uAMBHxz4y236m9Ay0Lto+fXnRnqF+hmG0fZmn06xvRl5VXq8qOoBhnk5OdU6fky0AEEURp0tOO838nJYen/g4mvRNeD/lfXuHYnFMdIiczHDf4bg86nK8uedNlNSVdOuYU2WnUN1kSHAs8YbQXaX1pXjz0JuYGjIVUyOm4vsz37cZVjGQ5VTnoFlsRrRndJf7KmVKPJr0KJKLk/Hz2Z+tFlNFYwVUchUUUkWvzxGhjoBSpsSpUstNEiaijomiiN35u+EidcGO3B1mz70zpWes8uHdlOj04QuNoroiNIvNvZqjA/w1T8cSbabLGspQXl/udBUdAIj2isYVUVdg5YmVKKsvs3c4FsVEh8gJ/T3p76jT1WFF8opu7d/yTeB89XlrhdXG6wdfR3VTNZ4e+zRuSrwJ5yrP4UjREZtd39FlVRqaDXSnogMA82PmI8EzAS8feBlN+iarxFTeUA5P174tKiiVSDHYd7BFOjIRUdeyqrKQX5OPpy58CkqpEh8f/9h0W1ppmlU+vKtd1Ah2D+7TFxr5tfkAet5a2miw92C4ydywP39/r2MwOltxFoDzdFxr7f7h96NWV2v22HAGTHSInFCMNgY3Jt6IT49/isLawi73P1BwAEqpEoDtKjoni05i5cmVuDHhRsR7xWP+4PlQSBQcvtaCcUhfdxMdqUSKJ8Y8gYzKDHyV+pVVYqpoqICXq1efzzPUb6hF274SUcf25O0BAFw16CpcGXMlVp1ZhfKGctTr6pFdkW21KkW8Z3yfKjp51XkAep/oyCQyJPknWWSejjHRidJ07/W4v0nwSsCs8Fn4+PjHPZ7j68iY6BA5qaenPI0mfRPePvJ2l/seLDyIKSFTIJfILZ7oNDY34kzJmTZD0h7d9CjcZG54NOlRAIZ2xReFX4Q16WusVo3ob7KqsqCQKBCsDu72MTNCZ2BswFi8ceiNPs+3qmysxNpTa83W7ilvKLdMouM7FPm1+U43TILIEe3J3wNPF08M8h2EhUMWor65Hv9L/R8yKzMt3lq6pUFeg5BWntbr1/S+VnQAw/C1k6UnUdlQ2etziKKIb059gwD3AKdpLd2e+4ffj/KGcrx74F17h2IxTHSInFSMVwyujbsWn5/8HLnVuR3uV1xXjIzKDIz2H40QdYhFh67tyNmBi1dfjLi34zDmf2Pw9O6nsTdvL37P/h3rz6zHgyMehLert2n/+THzUVJfYvXOYf1FZmUmQj1CIRG6/1ItCAKeGP0EiuqK8PnRz/t0/ZUnVuLyry/Hdeuvw/kqw+OivKEcnsq+DV0DgET/RACWWTmdBq7159bj1/Rf7R2Gw9uTtwfjAsdBIkgwxHsIxgeOxycnPkFaxZ+tpa2U6MR7xqNJ34RzFed6dXxeTR7kEjm8lL3/cmV0wGjoRT1e3/16r+eAbs/djj35e/DkpCchl8h7HYujG+k3EhMCJ+C/+/9r1aY2tsREh8iJPTTiIYgQ8Z8j/+lwn4MFBwEAo/xHIVwbbpGKTm5VLu7dci+uW38dmpqb8OrFr2Koz1B8cfILXPXzVbhp402I8ozCbUNvMztuWug0aF20WH1mdZ9jcAaZVZm9+vZwlP8oDPIahE+Pftqn62dVZcFV5oqU4hRc9P1F+P7M95ar6FhgojINbE36JizevhgPbnzQ3qE4tJzqHGRVZWFc4DjTttuG3Iac6hx8kPIBAEODEGtI8DJ0WDxZerJXx+fV5CFYHdyjL3tamxg4EZdHXY6lW5fi4a0Po6G5oUfHi6KI5QeWI1AViDuT7ux1HP3FdfHXIbMi0yINHBwBEx0iJxbiEYLr46/H16e+RkZ5Rrv7HCg4ALlEjkSfRIRrwvtc0fnm9DdIeDsBGzM24pGRj2Dz1Zvx9wl/xyczP0HyTclYMX0F5sfMx0eXfwQXqYvZsS5SF8yJnIONmRtR01TTpzj6O1EUkVWZhXCPnic6giDgmthrsC9nX5/WqsmpzsFQv6HYdNUmJHgl4IE/HkBBbYFFKjrBHsHQKDRsSEC9tit3F8obypFanIrsqmx7h+Ow9ubvBQCMC/gr0ZkZPhPB7sHYX7Af3q7eFm8tbRStiYZUkPa6cptXk4cQdUifYpBKpFgxfQWenfIsvjvzHa5bd123O5ICwJbsLThUeAgPjngQSpmyT7H0B5dEXAI3uRtWpznHF45MdIic3AMjHoBEkOD5rc+3e/uBggNI9EmEUqZEmCYMBbUFvR5PXVZfhid2PIELAi7Alqu34O9JfzdbQdpd4Y4roq/Af6b9B1MjprZ7jvkx81Gnq8PGjI29isFZlDWUoaqpCmHqsF4dPz92PmQSGb49/W2vY8ipzkGoJhRh6jB8f9n3eGL0E5BL5EjwSej1OY0EQUC8ZzxbTFOvrTu3zjSM6I/zf9g3GAe2J28P1Ao1BnkNMm2TSWS4dfCtAIBY71irXVspUyJSE9nrLzTyavIQ7NH9OYodEQQBz0x9Biumr0BKcQrmrJmD44XHuzxOFEW8evBVhHmE4bq46/ocR3+gkqswL2Ee1p5di8bmRnuH02dMdIicXKAqEDcNugmfHf3M1K7YqLG5EUeLj2KU/ygAQLgmHHpRj/ya/F5d64e0H9DQ3IC3L3m710MhRgeMRoh7SKffJjXrm/HZic8w7btpmPn5TDy/93msTluNU6WnzCbO92eZlZkAej+kxMfVB3Ni5+D7M99Dp9f1+HhRFJFTnWNKtKQSKe4ffj9O/u0kFo5Y2KuYWov3MnRk4tpJ1FM6vQ4bMzbikohLEKGNwO/Zv9s7JIe1J28PRvuPhlQiNdt+ffz1UEqVFvniojMJngm9quh8c+obZFZmIs47zmKxXBF9Bb6f+z0amhtwzXfXdDkP5cdTPyK5OBkPjXyoT2uH9Tc3Jd6E8oZyp3heMdEhGgDuGXaPoXzfal2dw3mH0dDcYEp0wjSGD7W9Gb4miiK+OvUVLvC5ABcEXNDrWCWCBFfGXIltOdtwrqztBNYjRUcw98e5eHLnk1DJVSipK8HHxz7Got8XYfr303HX2rt6fW1HYkx0wjx6V9EBgFuH34rCusJefdtd0ViBWl2t6TFh5Cpz7dN4+ZYSvBJQ2ViJvJo8i5yPBo69+XtRUl+COZFzMDt6Nnbk7nCKb58tLb86H+kV6RgfOL7NbZ5KT6yeuxovTHvBqjEkeCUgszITNY3dH478fsr7eGTbI5gcPBmPT3zcovEM9x2OJeOW4GTxyU4/yOtFPZ7+/WlEaaIwP2a+RWNwdBdHXwxvpTe+T/ve3qH0GRMdogEgUBWIWy+4Fd+c+gYFtQWm7buydwEAkvySAADhWsN8kN40JDhafBQnS0/i+oTr+xzv/Jj5ECAg6j9RmPbdNDy2/TF8e/pb3L32bly25jLk1+ZjxfQVWHvFWhy86yBOLzyNzfM3Y4TfCBwpONLn6zuCzCpDotOXVqZzYufAW+mNb05/0+NjjY+B1omOJSV4Gr5JZuc16qn159ZDKVVieuh0XBJ7CWqaarC/oO+LQjqb7ZmGDpYtGxG0dIHvBT1qX98bCZ4JECHiRNGJLvcVRREv738ZS/csxWWRl+GTWZ9ApVBZPKa5UXMRog7BeynvdbjPz2d/RkphCh4Z+QhkEpnFY3BkMokMV0Rfgd+yfkNlY+/bcjsCJjpEA8RjEx+DTtTh/ZT3Tdt2nd+FUPdQBKgCAACh6lAAMLUS7omvUr+Cq8wV86Ln9TnWWM9YrJ+3HsumL0OIRwjWnl2Lh7c+jI8Of4Q7ht6BrddsxRXRV0AQBAAwzBvxSsBgr8HIrnCOScmZlZnwc/Uzm+PUU3KpHFfGXIlNmZtQWl/ao2ONiU6oJrTX1+9KvGc8ALAhAfWIXtRjQ8YGTA+dDje5G6ZFTINcIscf2X/YOzSHszVzK9xkbhjqM9RuMcR7GZ7nxwqPdbqfXtTj/vX34z9H/oMb4m/Aiukr2jSssRS5RI4HxjyAnbk7caykbVz1unosP7gcg30H4/Koy60Sg6O7KuYqNDQ3YP259fYOpU+Y6BANENFe0ZgXPQ8rT6xEaX0pRFHEruxdpmFrAOAqd4WPq0+PKzo1TTVYk74Gl0ddDg+Fh0XiHeozFE9e+CQ+n/05jt9yHJvnb8aZRWfw7PhnO7xGoCoQRbVFqNfVWyQGe8qqyrLIwnTXxV2HJn0T1qSt6dFxxrWXrFnR8VR6IsAtgC2mqUd2Z+9GQW0BLo28FADg4eKBMQFj8Pv5/j+fwNK2ZW7DaP/Rdl37JdwjHEqpEimFKZ3ut+LoCqw4sAL3DrsXr1z4Sps5RZZ2Z9KdUMlVeC+5bVXn1YOv4mzFWbwx6w2rx+GohvsOR6Q6Et+f6d/D15joEA0g911wH2p1tfjk+CfIqc5BblWuWaIDACHuIT1OdNaeXYuaphqLDFtrj0SQIMErAZGenS9qF+QeBABmw/P6q8zKzD7NzzEa7D0YiT6J+PZMz7qv5VTnQCFRwE/l1+cYOhPvGc+KDvXI9ye/h0KiwEVhF5m2TQuZhpOlJznfq4XS+lKkFKZgbOBYu8YhlUgR5xnXaUUnpzoHbx5+E/MS5uGpsU+ZqvXWpFVqsSB+AX5K/8nscbO/YD/eTX4XNybciJnRM60eh6MSBAHzY+djd95unK+03ELitsZEh2gASfBKwKzwWfj4+MemCeqtE50g96AeNyP4KvUrxGpjMcpvVNc7W1GgKhAAkFuTa9c4+qpB14C8mjyLVHQA4NrYa5FSnILkguRuH5Nbk4sg9yCLNR7oSIJXAs6Un3GabnlkXaIo4vuT32NyyGSzyu7U0KkAwOFrLezPN8xZaq8Rga3Fe8Z3mugs3bMUoijijVlv2DAq4I4hd0APPT45/gkAoE5Xh4f/eBjB7sF4euzTNo3FEc2LngcRIv6X8j97h9JrTHSIBphFwxehvKEcL+1/CSq5yrRytZGxotPdlr/HC4/jYOFBXB9/vU2+hetMkMpQ0env3+pmlGdAhGixRGdezDzIJXI88ssjbb697EhOdY4pcbSmeK94NDQ3IKMyw+rXsrbXDr6GPef32DsMp3a0+CiyKrJMw9aMEjwTEKAKcIrha6IoWqTl+u683VDKlLjAt/ddMC0lwSsBedV57c4V3JazDevOrcP9w+9HhDbCpnGFqcNwScQl+OLkF6hurMbL+1/GucpzeG3ya3BXuNs0FkcUqYnESL+R+CLlC3uH0mtMdIgGmBF+I3Bh8IUobyjHmOAxbbrJBLsHo765vtuT1z86/BHkEjmujr3aGuH2iKmiU92/KzrpZekADGPbLcFL6YUHhj+A3ed3454t92DUV6Mw7n/jcMdPd6ChuaHdY3KqcxDsbt1uTAAwyNOwiGF/n6dT1ViF1w+9jsv/d3m/T7Qd2bqz6yCTyDAzzHxIkSAImBYyDdtztvdq3ShHsvLkSoS/GY7kou5XYNuzM3cnxoWMs9qE/p4wJlv/2PEP1DT91Wa6sbkR/9z5T0SoI3DPsHvsEtvdiXejorECd/98Nz489iFuHXwrJgVPskssjmh+zHwkFyTjjk13YHvO9n637hkTHaIBaNHwRQCACaET2twW4h4CoHtr6TQ0N2Dl0ZWYFT4L3q7elg2yF1RyFbRKbb//oHm27CyAvrWWbu2RpEdQ/ng51s1bh2fHPYsoTRQ+OvwRDhcebrOvTq9Dfm2+TRKdWM9YCBBwqrR/t5g2PuaKaotwz+Z70KRvsnNEzkcURazPWI/pkdPhqfRsc/u00GmobKzsVVVtd95u1DbVWiLMPludthrZldm4Zt012J23u1fnyKnOwYnSE5gTO8fC0fXOuIBxePmil7EhYwPm/jgXZysMr3H/3vNvpFekY+n4pVDKlHaJLck/CaP8R+GrlK8Q5hGGJ8c8aZc4HNUNCTfgiUlPYE/eHixYvwBTV03Fx8c+dpjnS1eY6BANQBMCJ2DF9BV4cOyDbW4zJjrdaUiwPWc7SupKsCB+gcVj7K0QdYhTJDpKqRK+rr4WPa9cKsdw3+G4M/FOvDblNQDtt3YuqC2AXtTbJNFxlbkiXB3e7ys6+TX5AIB7Rt2D/QX78eK+F+0ckfM5XnocGZUZmD+o/cUbJwVNglSQYsOZDT067ztH38HVP1+NF7ZZd+HM7qhsrMThwsNYOHwhAlWBuGnDTdiUuanH5zEeMzdurqVD7BVBEPDYxMfw5SVforC2EJf+cCm+Sv0KS7cuxcVhF5s1lrCHB0c8CI2LBq9PeR0queXX7enPFFIFXpzxIg7ccABvTHkD7nJ3LNm9BMt3Lrd3aN3CRIdoABIEAVdEXwFfVdsP0sbOZd2p6BwrNkwuHe0/2rIB9kGoOrTfNyNIL0tHuDrcqnOeAtwCoFVqcbL0ZJvbjEP/glXWT3QAYJDXoH7feS2v1pBcPzL+ESwcvBDvp7yPdefW2Tkq5/Lz2Z8hFaS4MuHKdm/XuGiQ5JeEjekbu33OL1O/xAv7XjAkSGk9S5D25e/D5rObe3RMV3bn7kaz2Iy/XfA3rJ67GvGe8bh90+1Ynba6R+fZlLUJUZooxPvEWzS+vpocPBkbrtyACE0EFm9fDJ1eh6Xjl9o7LEwPnY6ixUUdLqxKgFKmxLVx12LdvHX4/erf8cDYB+wdUrcw0SEiM54unnCTuXWronOq7BQitZEONWnTWSo6lhy21h5BEJDol4hTZW2HjBn/9sak19riPeNxrvJcv17/yFjRCfYIxtPjnsYIvxF4ZOsjOFNyxs6ROQdRFLH27FpMDJrY7hc0RtNCp+FQ3iG8m/wu6nR1nZ7zx/Qf8fj2xzE9dDoeGvEQjuQfQVFtUbfjeXjrw7hlzS0WnbOwLWcbXGWuGBcyDl5KL3w751uMDRiLRb8vws+nf+7WOaobq7ErdxcuDrvYYnFZUqhHKH6Y+wP+b9j/4Z0571j9ta675FL7rTXU38R5xsHTte3wUUfERIeIzAiCgGD34G4lOqmlqUj0T7RBVN0Xqg5FcV1xh5PsHZ0oijhbdtYia+h0xZjotP6gZkp0VLZJdBK8EqAX9ThZ9Fd1qbapFqeK+8+8nbyaPGhdtHCVu0IhVeC9Ge9BLpHjqm+vQlVjlb3D6/eOlxiGrc2N6nwo1k2DbsLM6Jl4fu/zmPD1BHx07KN2E+gNZzbggd8fwJiAMXj/ovdNQ6e25WzrVjzHSo4hozIDuVW5OFF6oud3qAPbcrZhfOB4uMgMDQTcFe74fPbniNJEYenWpd1KqrbmbEWjvhEzwx13DRhXmSuWjF2ChSMW2jsUcnJMdIiojRD3kC6HrjU0NyC9Ih1DfYfaKKruCVEb5hgZv2Hvb4rqilDbVIsIdYTVr5Xon4jKxso2Q/1ya3KhddHarFKX4GlocX6s8BjSy9Px9O6nkfRVEoasGNJvFn/Nr8k3a8cd7B6MFTNW4GTRSdyz+Z5+3wnM3taeXQupIMXsiNmd7uel9MIvN/2C1ZetRrQ2Gk/vfhoTv52IBasW4N4t9+LeLffins33YP6385HglYBPZ30KV5krhvoMha+br2l9se7GAwCbsywzfC2nOgdnK87iwuALzbYrZUrcnXg3DuQe6FZzgl8zf4XWRdtmjTSigYiJDhG1EeQe1GVFJ608Dc1is8NVdIyJTn8dvpZZlQkANqnoDPUzJKmt5+nkVOfYrJoDABGaCCgkCizetBiTv5uMz45/hhhtDJrFZmRWZtosjr7Ir81HgFuA2bbJwZPxzpx38Pv53/HPXf/sd21ZHYVx2NqFwRfCS+nVrWPGBo7FqstW4ZtLv0GsNhaH8w8jpTgFKcUpOFZyDJPCJuGrS76CWqEGAEgECS6Ovhjbc7ZDL+q7jOen9J8wJWQKRgaOxJbsLX2+j4ChuQtgeNy0dnXs1fBT+eGd5Hc6PUezvhmbszZjeuj0NksHEA1ETHSIqI0Q9xCU1pd22j7SOLcj0c+xEp1QTSgA9NuGBMYP9rYYt25MdFq3ds6pzrHZ/BwAkEvkGBNgWNPp0aRHsf+G/XhjyhumWPqD1hUdozuT7sR9F9yHz09+jvdT3rdDZP1fSnEKMqsyuxy21p5JwZPw9aVf49T9p7D92u2m/369+dc2LfFnRs1EUV1Rl0PRjhYfRXZ1Ni6LugxzYufgYOHBbq871pltOdvg7+aPeM+2DQSUMiUWjVmELdlbOm3Fvvv8bpQ1lDns/BwiW2OiQ0RthHgYqiKdJQuppamQS+SI846zVVjdEuxh6BSWV90/KzpZlVkQIJjafFuTVqlFkCqoTWvn3Jpcm7SWbunrS79G1sNZeHjkw/Bz8zNVlLrT/c/eGpsbUVRXhABVQLu3/2P0PzAncg6e3/s8NpzrWWcvMgwTkwkyzAqfZdXrzIw2zGnZdr7zeTprz66FXCLHrPBZuDT2UuhFfZfHdEUv6rE9ZzsmBU3qsNviPaPugavMFe+mvNtxbKcMsU0NndqneIicBRMdImrD2Fb4fFXHHzJPlp5EjDbG4TrVeLh4QK1Q99uhaxmVGQhWB9ts8bwErwSzoWvVjdUobyi3WWtpI0EQIBH+ektyk7vB29W7X1R0CmsLAaDDREciSPDvqf/GcL/huP/3+3Gs8Jgtw+vXjMPWJodMbneRUEsK9AjEIK9Bnc7TMcUTPBlaFy1GB42Gl9ILm7P7Nk/naP5RlNaXYnJI22FrRt5u3rg+/nr8kPZDh69vP53+CeMCx5mG5BENdEx0iKgNY0Wns2/TU0tTTZPIHU2QKqjfDl3LqspCtGe0za6X4JmAtPI0NOmbAADZFdkAYPOKTnvCteH9ItExrqHT3tA1I1eZKz6d+SkEQcB/9/3XVqH1ewdyD5iGidnClJAp2J+/v8Nhu/ty9iGnOsc0jE4qkWJqyFT8nv07mvXNvb7uprOGBT4nBU3qdL87h96JZrEZHx37qM1tZyvOIrU4FTPDHLfbGpGtMdEhojb83fwhFaQdfsg0dupK8HLMRCdQFdhvKzqZlZmI8oyy2fUSvBLQpG/CuYpzAICsiiwAtltDpzNhmjDT4qW2JIoiTpec7vb+xg5/rZsRtObj6oOZ4TPx7Ylv0djc2KcYB4pvj39rGiZmC1OCp6BR39hhd7Nvj38LhURh1rp5RugMlDWU4UjRkV5fd9PZTYj3jO+wKmgUpg7DZZGX4YuTX6CyodL8HJmGZOnicM7PITJiokNEbcgkMgSoAjqs6BgnwzpyotMfKzoVDRUorCu06bwn49/QOHzNmOg4QkUnTB2G89Xnbd6t7If0HxD/dnybbnQdMSbVXX1IBYAro69EaV1pt9drGchEUcS3J741DROzhTEBY6CUKrH1/NY2t+lFPb478R2mhEyBxkVj2j4lZAokgqTXw9fqdHXYnrm9TVvpjtwz7B5UNVXh9d2vmy2KuilrExL9EhHqEdqrOIicERMdImpXsCq4w2/TjR8AB3kNsmVI3RbkHtQvFw1NLTU0BRjmP8xm14zRxkAqSE3XzqrIgkSQwN/N32YxdCRME4bqpmpUNlZ2vbMFfX3qawDA3ry93do/vyYfLlIXeLp0PYdkSsgUeLl6YU3amr6E6JR+y/oN68+sR3p5OhqbG3G46DCyKrJ61W2tt5QyJcYFjsPWnLaJzqHCQ8iuzG4Tj6fSE0l+Sb1uM72/YD8amhvabSvdnmG+w3Bh8IVYunUpYj6JQdKXSbhq7VXYl78Pc+Ns97si6g/YZJ2I2hXiEYJ9+fvavS21LBUecg+brrXSE8a5EgU1BYhEpJ2j6T5jAjnMfxhQbZtrukhdEKWJMnVey67MRoBbgEOswRGuNbTYzqnOMfsG3Zpyq3OxK3cXAOBg4UHcOuTWLo/JrzW0lu6oW1ZLCqkC1wy+BiuPrkRNUw1UclVfQ3YKZ8rO4G+//M30s0SQQCVTQSE1HyZmC1NCpmDpnqXIqsiCrMXHpLVn18JF6tJuPDPCZuBf+/+FvKqeD5ndfn475BI5xgeO7/YxH170IQ5XH8bhzMPIqMxARmUGItQRuGnYTUBTj0Mgclqs6BBRu4Ldg5FXk9fuiu6nSk8h3iu+Wx/s7MGYgPW34WsnSk9A66I1tci2lQSvBNNwxKyKLIeYnwMYKjqAbdfS+SHtB4gQMTxgOA4VHurWMfk1bRcL7cwNiTegTleHXzN/7W2YTsc4J+a7a77DG1PewAPDH8CMsBlYNn2ZzZJco6khUwEAv6b/9ffJq8nDz+d+xuyY2fBQeLQ5ZnrodADAhrSetw/flrMNE0InwE3u1u1j3BXuuG7odXhgxAN4fcrrWD13NbZduw2DfB2zyk5kL/b/yo6IHFKwezCaxWbkVeVBCqlpuyiKSC1LxWWRtumC1BvGik5/a0iQWpqKQV6DbJ5AJngmYO3ZtahpqkFWRRaGeg216fU7YutERxRFrDqzCkl+Sbhu6HV47LfHUFpfCi+lV6fH5dfkY4TfiG5fZ1LYJASpgvBD2g+4MubKvobtFPbm70WAWwDmD5qPPO1fz9ugoCDk5tr2C4tYbSwCVAF47+B72OC2Afvz9yO72tCN8Nbht7Z7zGCvwQhQBWD9mfWY7T+729f6Les3HCs5hteTXrdE6ETUCis6RNQu44KVmRWZZttzq3JR3lDusPNzgP6Z6OhFPVLLUu3yezU2JEgtTUV2ZbbN19DpiJ/KDwqJosNE50jRETy26THU6+otcr1jJcdwuvw0ro69GuNCxgEADhYc7PQYURSRX5vfrUYERhJBgnnR87D1/FaU1pf2KWZnIIoi9uTvwdjAsQ5RJRYEAbPCZ+FA7gFsz9mORN9EPDPuGWy8ciPmJczr8JgZoTPwa/qv2Hp+K7ad32b6r7y+vN1jappq8OTOJxGnjcN9Y+6z3h0iGsBY0SGidsV5xkGAgJ9P/4wHBj9g2p5SmAIAiPeKt1doXXJXuEOtUNulNXFvZVdlo6apxi6d7IzX3JG7A43NjQ7RcQ0wJARB7kHIqWk/0fkq9St8mfol9mftx4cXfwhXmWufrrfqzCooJArMjZqLyKBISAUpDhUe6rRdb1lDGRqaGzpdQ6c982LmYUXyCqw9uxZDoxyjgmYvWVVZyK/Jx9iAsfYOxeTpsU/juZnPQVot7XbyNTN8Jr5M/RI3bLjBbHv0nmisunQV/Nz8zLYvP7AcOdU5WDN3DRRShcViJ6K/dCvRWbp0KSoqKiCVGoav3HXXXSgoKMD3338PnU6HOXPmYPbstqXajIwMvPfee6itrcWgQYNw5513ms5BRI4t2D0YV0Rfgbf2vYUbIm+Aj6sPAJhWdXfUxUKN+ttaOvbsZBfmEQY3mRs2Zxna4zrKHB3A8DjsqKJzovQEfN18sfX8Vvztl7/h05mf9mieQ0s6vQ4/pv+IGWEz4Kn0hJvcDYO9B3c5T8fUWroHc3QAw1CnOG0c1qStwRMXPdGrmJ3Fnvw9AIBxAePsHMlflDIlgjQ9W3h4RugMHLzrILLzsk3bCusK8fDWh3HDhhuw6rJVpjbZyUXJ+Oj4R7h50M0YHTDa0uET0Z+6HLomiiJyc3OxfPly03/e3t743//+h+effx7Lly/H5s2bcf582/U23nrrLSxcuBD//ve/IYoiNm/uXY95IrKPh0c+jHpdPf579K+V3FMKUxDgFgBPZdetdO2pv62lc7L0JAQIiPe0faVMIkgQ7xmPw0WHATheotPeek56UY9TpaewYOgC/Hvqv7E7bzdu2ngTqht7165uU/omFNUV4erYq03bRvqNxJGiI52ueG9aLLQHQ9cAw1CneTHzsK9gHzLLM7s+wIntzdsLTxdPxHrG2juUPhEEASMDR2J0wGjTf3Mi5+DHBT8ivTwdN2+8GTVNNdDpdVi8fTF8XX3xxOiBneQSWVuXiY5xEuCLL76IxYsXY+PGjUhOTsbQoUPh7u4OpVKJsWPHYs+ePWbHFRUVobGxEXFxhoXvpk6dit27219pmIgcU4w2BjcPuxkrT6w0faBLKUhx2IVCWwpSBfWris6J0hMIV4fbrd1wvGc89KIeABxmjg5gSHQKagrQpDfvmZtVlYVaXS2G+Q/D/Nj5eHva2zhQcAA3bLgBhTWFPb7OyuSV0LpoTd2zAEOiU91UjTPlZzo8zvgY6+nQNcCweCgAfH3s6x4f60z25u/F2ICxkAjOOW14RtQMrJi+AkeKjuD2TbfjlZ2v4FjJMTw/4Xmbd5QjGmi6fFWpqalBYmIiFi9ejCVLlmDTpk0oLi6Gp+df3+Z6enqipKTE7LiysjJotVqzfUpLOemSqL95esrT0Ol1eOvIW9DpdThRdMIuVYeeClQFoqiuCI3NjfYOpVtOlpzEYK/Bdru+MXlVyVU2W4W+O0LcQyBCNCXaRsYFThP9EgEAV0RfgXdmvIOjRUcR8WYEntr5FLIqs7p1jarGKqxJXYPLoy43mysx0m8kAHQ6fC2/Nh8ChDbzL7ojTB2GUf6jsHTrUizethjHSo71+Bz9XX5NPjIqMzAmYIy9Q7GqSyIvwWuTX8P2nO14astTmBk+E5dGXGrvsIicXpeJTlxcHO6//34olUqo1WpMmzYN3377bZv9Wk/WE0Wxy32IyPFFeUbhuvjr8FXqV9iVuwsNzQ39oqJj/IY9p9J2a7D0Vm1TLTIqM+zayc74Nw3VhDrUa7VxGF3reTrGoX5D/IaYts2JnIPNV2/G9UOvx5epX2LSt5Nw35b7cLzweKfXWH9uPep19WbD1gAgUh0JTxfPTjuv5dfkw9fVF3KJvKd3DQDw9rS3cWPijVidthqzVs/CvJ/m4af0n9p9D3VGe/P3AgDGBTrO/BxruTbuWiybsAwJPgl4YcILDvU8I3JWXSY6qampSElJMdvm6+uL8vJy089lZWXw8jJfZ8DLy6vNPi2rQETUfzw44kEAwOLtiwHYZ8J8Txk/IJ+vbDu/w9GcKjsFEaJdf6/GaxvXrnEUxmF0refpnCw9iXB1ONwV7mbbY7Qx+OiKj7B7wW7cmXgnNmVtwqgPRpkaLbSWU52Dt468hVivWFMFx0gQBIz0G9l5RaemZ62lWwv1CMUHl3+AgzcexDPjnkFRXRHu2XIPVp9c3eExoigirTSt19d0JHvz90IlV2GI95Cud3YCtw65FSfvO+kwnQ2JnF23hq598cUXaGxsRF1dHbZu3YpFixYhJSUFlZWVaGhowN69ezF8+HCz43x9faFQKJCaahhesHXrVowY0f0F1YjIcQS7B+OGhBtwvvo8JIIEMdoYe4fUJWNFJ7syu4s97c84DGuQt/0SHR9XH4S6h2KQj2MlsZ1VdDpLDANVgVgydgl2XbcLQ3yH4LZfb8OatDVm+xwrOYbLf7wcJfUl+Ojyj9r9hn2k30icLj+NioaKdq+TV5PXq/k5rWldtLgr8S5su2Ybwj3C8ebeNzvc94NjHyD2rVj8c+c/28xd6m/25u3FaP/RkEm42gURWV6XiU5SUhJGjBiBxx9/HP/4xz8wbdo0JCQk4Prrr8fSpUvx2GOPYdKkSYiJMXzweemll5Ceng4AWLRoET777DM8/PDDaGhowCWXXGLde0NEVrNo+CIopUrEeMX0eb0SWzB++OwPFZ2TpSfhJnNDmId9qyk/XfETlk1fZtcYWnOVucJb6W2W6NTp6pBRmdGtIZQ+rj7Y8rctGB0wGvf/fj8+O/EZAEOXtflr50MQBPww9wdcGH5hu8eP9DdUeY4WHW339p4uFtoVqUSKW4fcih1ZO5BSnNLm9npdPd45+g78VH745MQnuPbna1FY23XzBb2oxx/Zf/S6K501lNSWILUs1aHWzyEi59Ktr1AWLFiABQsWmG2bNGkSJk2a1GbfJ574q1ViREQEXnrppT6GSESOIEAVgJcmvQQfLx97h9ItHgoPeMg9+kWic6L0BOK94u3edcrPzQ8qhQoVaL96YS8h7iFmi7+eLjsNvajv9lA/tYsan8/+HPdsvgdP7nwSe/P3Yt25dYjVxuLz2Z93WpEZ7jscAgQcLDyIBTB/H6xrqkN5Q3mP19DpynVx1+HVg6/i4+Mf440pb5jd9s3pb1BYV4gtt2zByeyT+Pu2v2P2D7Px/kXv4/Kgy9s9X3p5OhZvX4y9+XvxYNmDeGzYYxaNt7d2Zu8EMDDm5xCRfThnL0cisopr467FTcNusncY3RaoCnT4oWuiKOJkqX07rjm61mvpGIf69WTRWleZKz64+ANcFXMVfkz/EVPCp2D13NVdDjtTK9SI84xrd55OTpWhymTJig4AaFw0+NsFf8OatDUoris2bW/SN+Gdo+9gpN9ITI2Yinkx87D2irVQSpW4+uercc/P9+DXzF9R01QDwLAI6oqjKzBz9UyklqYi0ScRnx75FLVNtRaNt7e2ZW6Di9QFF/heYO9QiMhJMdEhIqcV5B7k8BWd3KpclDeU94sGD/YS5B6EnOocUyeyE6UnoJQqEaGO6NF55BI5/j3131h12Sqsv3E91Ap1t44zNiRo3QnN+NiyxByd1u4fcz8a9Y34MvVL07Y1aWuQXZ2NB4Y/YJpPNNh7MNZfuR5zo+bi8+TPsfDXhRiycgiu/vlqjPlgDJbtW4ZpodPw+zW/4/nxz6OioQJr0tdYPN7e2Ja5DSN8R8BF6mLvUIjISTHRISKnFagKdPhEJ7kgGUD/6GRnL8HuwajV1aK8oRyAoaIT7xkPqUTa43NJBAnGB443Wy+nK0l+SShvKMeZUvOFQ42ty62R6AzyHYQpwVOw8sRKNOmboBf1ePvo2xjkNQgXhV1ktq/WRYu3pr2FksdK8O2cb3Hn0DtR0VCB0rpSvDvjXXxw0Qfwd/PHKP9RGOY/DJ+e+NTu7aurG6txKO8QxgZyfg4RWQ8THSJyWoGqQBRUFzj0oqHGRKc/rE1kLyHuIQCAnBpDYtFVxzVLM7ad3nN+j9l209A1C8/RMbpt6G3Ir83H+nPrsSZ1DdLK07Bo+KIO119xkblgYtBEPDX2KWyavwkZD2VgbtRc0/6CIODeUffieMnxTltm28LBwoNoFpsxLoDzc4jIepjoEJHTCnEPgQgRP539yd6hdCi5MBlBqiBoXbT2DsVhGdccyanKQUF1AUrqS2yaGMZ6xsJD7oFNZzeZbc+pzIG73L3NWj6WMj10OiLUEfjo2Ed4cfuLiFBH4LLIy/p0zhuH3Qh3ubup+5y9bMneAqkgRZJ/kl3jICLnxkSHiJzWpZGXYmLoRDz4x4N4/eDrdh+u056UghQOW+uCKdGpzrHLUD+JIMF18dfhq5SvcKr0lGl7TlWOVYattbzuwiELcbDwIA7mHcT9F9zfq+F6Lbkr3HF17NVYe3YtSutLLRRpz+TV5OGLk1/g+sTroZKr7BIDEQ0MTHSIyGl5KDyw+ZbNuDr2arx26DXcu+Ve1OnqABjWYlmdthrXr78eYz8ci2Z9s83ja2xuxMli2w7D6o+8ld5QSpXIqclBSqFhbRlb/84eHPEgPBQeeGHfC6Zt5yvPW7zjWmvXxl0LlVyFEHUI5sfOt8g5bxl0Cxr1jfj61NcWOV9PvXHoDehFPZ6f9rxdrk9EAweXIiYip+Yic8GbU95EnDYOL+1/CVlVWRgZPBLfHv8W1U3VcJW5ok5Xh4LaAgS5B9k0trTyNOj0OiY6XRAEwdR5ra6gDn6ufvB29bZpDF5KLzx14VN47LfHsCNnByYFT0JOVQ7G+4+36nXVCjXem/EeIoMie9RAoTPxXvEYHzgen5/8HM/Nfs4i5+yutPI0/O/U/7BwyEJEaCOQW5vb9UFERL3Eig4ROT1BEHDf8Pvw4cUf4lTZKXx74lvMiZyDVZetwrsz3gUA5NbY/gNXSomhOsFGBF0zrqWTUphit9/XorGLEOIeguf3Pg+dXoe8qjyrV3QAYFroNEwInWDRc94y6BZkVWXhl7RfLHrervxr/7/gJnPDg8MftOl1iWhgYqJDRAPG7IjZOHrTURQ8WoDXp7yO8YHjTfM/cqttn+isPrMaYZowxGpjbX7t/iZYFYysyiwcLzxutwqYUqbE46Mfx7GSY/gg5QM0i81WnaNjTbMjZsPP1Q8rDqyw2TX3nN+DDRkbcM+we2xekSOigYmJDhENKCq5Cm5yN9PPQSrDcDVbV3TOVpzFjtwduGvkXX2eYD4QBLsHo6S+BA3NDXatgM2LnodEn0QsP7gcgHXW0LEFhVSB+bHzsTFtI6oaq6x+PVEU8dimx+Dr6ou7Eu+y+vWIiAAmOkQ0wKkVaqjkKpsnOl+mfgmZIMNtI26z6XX7q2CPYNO/B3sNtlscEkGCJWOXoKG5AYD11tCxhYvCLoJOr8OOnB1Wv9bm7M3YnrUdD4982OyLBiIia2KiQ0QDmiAICNWEIq86z2bXrNfV45tT32BmxEwEevTPioCtBasMiY5UkCJGG2PXWCYGTcSM0BkA+m9FBwCS/JOgdlFjS/YWq17nWMkxLN2zFDFeMbgh4QarXouIqCV2XSOiAS9UHYrcKttVdNZnrEdZQxluHnSzza7Z3xnnUsV5x0EpU9o5GuCVC19Bck0yfN187R1Kr8klclwcdTG2ZG2BKIoQBMGi58+rycMrB17Bd6e/g9ZFi++u/Q5yidyi1yAi6gwrOkQ04IWqQy3ejKBOV4d3D7xrWrenpc9PfI4IdQQmBU2y6DWdmbFyMsx/mJ0jMQhQBeDW4bfaO4w+uzT2UuTX5ONk6UmLnbNeV49nfn8GF357IdakrcHdw+7Gzut2YkbUDItdg4ioO5joENGAF6oJRVFdERqbGy12zl8yfsE96+7Bwl8Xol5Xb9p+vPA49hXsw82DboZE4EtwdyllStwx9A4sHL7Q3qE4ldkxswEAv2f/brFzvrjvRTy37TlcFHYRtl6zFUvGLoHGRWOx8xMRdRffZYlowAtVh0KEiPyafIudM7k4GVJBih05O3Dnb3eaJq+/d/A9KCQKXBt3rcWuNVAsHb8Us2Jm2TsMpxLkEYTBXoPx+3nLJDqppan49MSnuGfUPXh3xrsIU4dZ5LxERL3BRIeIBrxQTSgAy7aYTi5ORlJQEl6+8GVsyd6Cu3+7GxUNFVh5dCUui7oMXkovi12LqC+mh03Hvvx9qGys7NN5RFHEM7ufgYfCA89Pe95C0RER9R4THSIa8ELVlk109KIex4qPYWTASNyYcCOWTVyGTVmbcMkPl6CioYJNCMihTA+ZjmaxGdtztvfpPL9k/oIduTvw95F/h7cbFwQlIvtjokNEA56pomOhhgSZlZmoaqpCUlASAODWwbfi2XHPIrMqE0N8h2C0/2iLXIfIEpL8k6BWqPs0T6deV4+le5Yi3jMetwy+xYLRERH1HttLE9GA565wh0ahsVhFJ6U4BQCQFJgEiIZtdybeiVCPUAyPHA5BtGwbX6K+kElkmBw8Gb9n/w5RFHt1jtd3v46sqix8fenXkEn40YKIHAMrOkREAILcgyxW0UkpToFCosAQvyFm22dHzMbIwJEWuQaRJU0PnY782nycKD3R42PzavLw4vYXcUnEJbgw+EIrREdE1DtMdIiIYFinxWIVnZIUJHglQCFVWOR8RNY2NXQqAGBL9pYeH/vivheh0+uwZOwSC0dFRNQ3THSIiAAEqYKQV5PX5/OIooiU4hQk+iRaICoi2/B388dQ76E9nqezLWcbVqetxuIJixGuDrdSdEREvcOBtEREMAxdK60vRZ2uDq4yV7Pb8mvycT7nPEKEkC7Pc776PMobypnoUL8zLXQaVhxdgZ9P/4yzeWdRVFuEwrpCzB40G2M1Y9vsX6erwxM7nkCkOhJPTX4KpYWldoiaiKhjTHSIiGCo6ACG+QZRmiiz25btW4afzv6Eby79BuMCx3V6nuTiZABgokP9zsVhF+OtI29h7v/mmrbJBBk+SPkAK6avwOXRl5vt/+bhN5FRmYFv53wLpUxp63CJiLrERIeICIaKDmBoMd060TlYcBA6vQ53b74bG+ZtMO3bnpTiFMgEGRI8E6waL5GlJfknYdVlq+Dv6w+hRoCfmx8kggR/++1vePCPB+Ht6o2JQRMBACdKTuDdo+/iurjrTNuIiBwN5+gQEeGvik7rhgQldSXIrMrEwuELUaerw12/3YWG5oYOz5NSnII4zzh+w0390vjA8ZgUNgmRmkio5Cq4ylzx0/U/IUIdgdt/vR0nSk6gWd+Mx3Y8Bo2LBv8c+097h0xE1CEmOkREMHRdA9ouGnq46DAA4Nbht+LNKW/icNFhPLXzqXbXGzE2IhjmM8z6ARPZiJerF7645AuoFCrctPEm/HPLP3G48DCWjl8KL6WXvcMjIuoQEx0iIgBKmRLeSu82FZ3DhYchFaRICkzCpZGX4oHhD+B/p/6HL1K/aHOO85XnUVJfwvk55HSC3YPx5ewvUaerw792/gtTQ6ZiXvQ8e4dFRNQpJjpERH8Kcm/bYvpw4WHEe8ZDpVABAB5NehTTQ6djya4l2J+z32zfQ3mHALARATmnBK8EfDrzU8yInIGXJr4EQRDsHRIRUaeY6BAR/SlIFWQ2dE0v6nG46DBG+I0wbZNKpHhr2lvwcfXBwh8Xms3XOZh3EBJBgsHeg20aN5GtjA0ci99u+Q1h6jB7h0JE1CUmOkREfwpSBZkNXTtbcRaVjZUY6TfSbD+tixYvT3oZx4uO460jb5m2H8o7hDhtXJt1eIiIiMj2mOgQEf0pUBWIysZKVDdWAwAOFRqGoo3wHdFm3xlhM3DTsJvw1uG3cKLkBABDRWeoz1DbBUxEREQdYqJDRPQn01o6f1Z1DhcehrvcHTHamHb3f2PWG9C4aPDotkeRU52D/Op8dlwjIiJyEEx0iIj+ZFpL5895OoeLDuMC3wsglUjb3d/HzQcvTHgBR4uP4pGtjwBgIwIiIiJHwUSHiOhPLSs6dU11OFly0qwRQXvmRs3F7PDZ2JG7AwIEDPEeYotQiYiIqAtMdIiI/hSgCoAAAbk1uTiUdwg6UYeRviM7PUYQBLw46UWoFWrE+8RDJVfZKFoiIiLqjMzeARAROQq5RA4/Nz/kVudib85eAOiyogMA/m7++GL2F/Dy5irxREREjoIVHSKiFowtpvfm7EWwezD83Py6dVySfxImhk20cnRERETUXazoEBG1EOgeiFOlp5BVndVuW2kiIiLqH1jRISJqIUgVhMzKTGRWZHZr2BoRERE5pm5XdD7//HNUVlZizpw5+O9//2vaXllZCXd3d7z22mtm+584cQKvvvoqvL29AQCRkZG49957LRQ2EZF1BKmCoBN1AIAkvyQ7R0NERES91a1EJyUlBX/88QdGjhyJiIgILF++HADQ0NCAJ598EnfeeWebY9LT0zF37lxceeWVlo2YiMiKjC2mZRIZhvoMtXM0RERE1FtdJjrV1dX4+uuvceWVVyIzM9Psth9++AGDBg1CQkJCm+PS09NRUVGB3bt3w9vbG7fffjt8fHwsFzkRkRUYFw0d5j8MrjJXO0dDREREvdXlHJ33338fCxYsgLu7u9n2mpoabN68Gddcc027x7m5ueHSSy/FK6+8ghEjRuDf//63ZSImIrIiY0VnbPBYO0dCREREfdFporN582Z4e3sjMTGxzW3bt2/H6NGjodFo2j32rrvuwujRowEAM2fORHZ2Nmpray0QMhGR9fi7+ePWwbfi9hG32zsUIiIi6oNOE51du3YhOTkZixcvxjfffIMDBw7g008/BQDs378fEyZMaPc4vV6P1atXQ6/Xm22XSqWWiZqIyEokggTLJi5DUhAbERAREfVnnc7RWbJkienff/zxB44fP45bb70Voiji7NmziIuLa/c4iUSCffv2ISAgABMmTMDWrVsRGxsLFxcXy0ZPRERERETUjl4tGFpZWQmZTAaFQmG2/ZtvvoGnpydmzpyJ++67D++99x5WrVoFjUaD++67zyIBExERERERdaXbic7UqVMxdepUAIBGo8EHH3zQZp/rrrvO9O/Q0FC88MILfY+QiIiIiIioh7rsukZERERERNTfMNEhIiIiIiKnw0SHiIiIiIicDhMdIiIiIiJyOkx0iIiIiIjI6TDRISIiIiIip8NEh4iIiIiInA4THSIiIiIicjpMdIiIiIiIyOkw0SEiIiIiIqfDRIeIiIiIiJwOEx0iIiIiInI6giiKor2DICIiIiIisiRWdIiIiIiIyOkw0SEiIiIiIqfDRIeIiIiIiJwOEx0iIiIiInI6THSIiIiIiMjpMNEhIiIiIiKnw0SHiIiIiIicDhMdIiIiIiJyOkx0iIiIiIjI6cjsHYAlfffdd9i9ezcAYOTIkXBxccHvv/+OiooKKBQKhIWFoaioCBUVFZBIJHBxcUFzczNqa2shCAJkMhlcXV1RV1eHpqYmiKIItVoNuVyOsrIy6PV6AIBUKoUoiqafAUAikXT6s70JggBRFO0dhs3Z835359r8uzjmtVvv0x//ToIgAIBZ3DKZDDqdrsPbBUGAIAg9fi3rzu/HUufpzd9CJpNBr9ebXV8qlaK5ublH55FIJBAEodPjLBVfd3R1re7cx9bn6Ojv1N7jxZKxdrSPJZ577cXe+n62d53e7NPfWOq1rafn6ej1p7+9zg4UHT2HAECv10MQBMjlcmi1WshkMpSVlUGn00EQBOh0Onh5ecHNzQ3l5eXQ6/Wor6+HVqvFpZdeissuuwwAUFpain/84x94//33LRq701R0kpOTkZycjFdeeQWvvPIKkpOTsWPHDuj1erz66qvw8fFBeno6Ghoa8Pzzz6O5uRlyuRz19fWYOnUqpFIpBEGARCKBTqeDKIrw9vaGQqFASUkJ9Hq9KTlq+UEgLi4O7u7upp+NCVPLB4O/v79ZrMYHTGsKhcL0b61W2+4+xgcWAERERHS5j0wmg0qlavNhp2UMwcHBkEql7Z7LyMXFBW5ubmb3QS6Xt9mv5XkjIyM7PScABAQEdLkPYIjZyNPTs919WsYze/ZsAL1/U+5Ie/cZaPs3NSbDLbX8uxi196ZupFQq2/xdWp9DIpFAqVR2eZ2W23x8fNq7C2Zan7MjHT2WW3JxcWmzrbO/i0ql6vJaLR+LnXF1dTX7WS6Xt3mhbv07br1Pe2++7u7ubWJr/bfrzu+mo8dTS935W7T+mxvvgzFu42uLMckxnrf1/QTQ5kNb659b3i+JRAIPD482ry/taf2Bsb370Pr33PJvI5PJ2ry2tt6nIwqFos31e5rkAICHh4fZcd15TneHTqfr1Yflrq7VnfvY+hwdPSZbPp56ozvHtrdPZ8d19FhrqfUHNEEQIJVKO01gjI+pzv4mCoWizXm683zujdavY0D7j72OeHh4mP3c8vnX0e+39eeW9o4F/npt6ew1oKPXwtbHtPy59Wtse+fq6L2ipZZ/E7Va3eX+oaGhXb6mtP7de3l5tbtfy890Go3G9O+Wv5+OrtWdx3ZvxMXFtbu95Wcxb29v07+N8clkMkilUtN7r5+fn+nx7+rqisGDB6OpqQkNDQ2mgkFoaCjuvfde0xcul1xyCUJCQqBUKvHSSy9BIpFg3bp1yM/Px6FDh7B06VKUl5db/D47TaLj6emJm2++2fRmGBwcjEGDBiEoKAiBgYEICQlBTEwMpFIpfH19MXToUAQGBsLV1RUJCQkIDw9HWFgY9Ho93N3d4enpCa1Wa/qwKZFIMHz4cNM2wPDAjYyMND2ABUHA6NGj4eXlZXrCajQaxMTEmJ6cxuSp5ZPV3d0drq6uZm9K9fX1ZvcvKCjI7Anr4eGB2NhYs32M9934wiuXyyGTyUwPTJlMBrlcDk9PT7MXlIiICNOLivGFo/ULiCAIqKurM51XKpWavWgYkyVjjFKpFHFxcWYvCJ6enmY/u7u7Izo62rTN3d0d/v7+Zvu4uLiYrme8360THeO3rJ29MLT3gValUpm2GY/v6AOYUXvJS8vjWr4otNb6TbP1m2LrD9iurq5tXkCNxxhjUiqVaGpqMtun5f00fgBvGY+fn5/p3y4uLu3eb51OZ7qG8QuA1mQymSkhb71Py99rSy33Mf67ZbwSiQTNzc2mbSqVqt0vDwIDA033yfj8NB5j/H/LNxmj1o8d43OvJePj2vj/9h4TrZ+fWq3WLL6Wv2NjLK3/3kql0pQEGmM1xm7cVxAENDU1mWJo+WbZ+lxGEokErq6uZnG3l2zqdDqz67Xex/j7bfl3ap2MiKKI2tpa089yudzsQ5Xxudvy3IIgQKVStfl9GF9zjF86tX5ctnx9NO6jUqlM5zbuazxPy2MbGxvb3JfWjHEbb3N1dTW9trSOob2ksOVju/Vjpr3XJ+Pv03h769eQ1o/rludtuU/L8xi1/N1KJBLTfy3jbHkuuVxu9u1sy8df6+u1dw3jvsZ4Wn5R1/p3qFAoTK+bLfdpeX6lUmkWs/Hx3Pp+GW9r+X/j+6Bxn9ZfOrV+/zW+d3X2RYogCHB3dzc7T3uvu15eXqbHbev7ZHyOu7i4tPlbGK9vjK3lfTf+Xo1aPy+NsbZ8DGg0GrPfr/H3ZvwdBQUFtXnMtPxwCwAlJSWm15uWf//WyclFF12E1lq/T7b+QrO919TW1w8ODm73edT69aezL5SMX+YY92loaGjzuan1a4GLi0ubhKj1c8bPzw+BgYGm2xsaGtq9fst9ampqTMe3/MKp5ePV+LeUSqVm+wDmv8OW97nl+1d773ktyWQy0+c443mMjw3j66ZEIkFlZaVpH+PfpampCRKJBJMmTYJUKkV5ebnpsanVahETEwNRFFFfX2/6AjYoKAgnT56ERCJBbW0tVq5cCW9vbzQ3N8Pb2xve3t5oamqCVCrFli1b8Pe//73T+HvLaRKd0NBQU6aal5eH5ORk+Pj4QKvVmn729fUFAFRXVyM9PR3e3t6QSqX45ZdfMGbMGOTk5MDFxQV6vR7BwcHIzs42VSX0ej18fHxQVVVlejAGBARg165diIqKAmB4UHt6eqK6utosruTkZNPPPj4+EEXR7EUoIiICjY2NpjdRjUYDvV5vto+LiwuamppMb6xBQUHYvn276QEfFBSE4ODgNk+ghoYG0zHGD/Y1NTVm++zbtw8VFRUA/noCtv7Wv+UTwsXFBTqdzuwJYyxHGuORy+XYtm2b6Ynn4eGBsLCwNgnWoUOHTNv8/f1RUlLS5k2xqanJ9EElKCgIGRkZbRIoURRN8QiCgI0bN5rFHxQUZPahxMPDA/X19abfeXvfxBk/PLb+MNMyPo1Gg+bmZtM+7Z2v9Yde4+PH+EYZHh5udqxReXk5ioqKAPz1ImQ8xni9uro603HGF+eWb8DGBMG4TSKRICMjw3R7Q0MDwsPDzf4uSqXS7D75+fm1qTDK5XLodDqUlZWZjjFWPVt/W97Rt4fG87f8/er1erMP9r6+vhBFsc2L/tmzZ03XaG5uNnszM25vbGw0e4wCbd8Iampq2nyoKSkpAWD+jWbrN9NRo0aZ/dz6W6js7Gyzn+VyeZsPRvX19aYkobGxEXK53BS78c1XFEWz6pDxedr6frR8QzV+KdHyG9Gqqip4e3ub3Y/Bgweb9nFzczM9x4zX1ul08PPzMx2jVqshiqLZtY0fzozPx6amJpSVlZke/8b7ZHwuGT+otNxmlJCQYPqQZ/ybt64iGx8/xr95Q0MD3N3dzW4bOXJkmw9ExvtpfE1t+Vpl/P/w4cMB/PWhJi4uDi4uLmbnaW5uNn2zCRi+yZVIJNBoNGZfbrX3LXlISIjp31Lp/7dzLbFtVO/+N+M4ju0kzsNO4qRpYlKaBJc+pECaVpQKVNRddg20RWzYIBZsKgFLEIgFSxYIsYAVCx5iUaRKbaWWPpD6Ik1a2qRNXfJOE+fhju3YMxnPXVjf13NmgnTv1UX63+j8pCiZ+MzMd17f4/d9xz4Eg0G0trayXD6fj3U/UJ77np4eaU2Ew2GEw2FPVsldkpdMJvm5m+0zcXwo2BftBM0ftXGTNBUVFZ6sJZEUNEYENwlAupgcNL/fz++j+1paWqRSvvb2dtZjotMpEjKk/yKRCAcMpVJJCuiB8l4T9wbJIwbjhmFIa5/2h5uUEa9t20Y2m/UEwdQnIq3a2trYvoslP5qmMUlKPgLtBbLZmqZ59jrJLWasMpmMpDNXVlY4CAWA2dlZhEIhaf6OHDnC/QLK+9/tB9BeF4PKixcvwg1N06RgYWlpie0cydrU1CQRqmTrCA8ePNiUWBT1hmVZ/0jOUh9ITpKL3iP6KiLoqIMIup+CpEAggPn5ef48l8t5yL1AIIDJyUlJFgrYRd1TLBZZTpE0dwei4rsdx+HgmGwcEdlin0i/EGzbxtzcHLfx+/3s49bU1HCGhmxVIBBg/xYAtm/fjocPHyIYDCIQCMA0TTQ1NWFtbQ2nT5/mIH///v2oqKjA7du3cf78ebS2tqKurg4dHR24ffs29uzZg/fffx/j4+NIJBKIxWI4deoUtm/fjn8DWybQIUxPT+Ozzz7D22+/jZaWFuRyOb6ur6+Hbdt8XV1djVwuh2g0inPnzuHVV19FJpNBIBBAKpVCMpnE/fv3eQFfuXKF2wDA1NQUhoaG8NdffwEol2pdvnwZAwMDLE8qlcLg4CBv2KmpKezevVuSOZVKSUrBNE0kk0nJmE1OTkoO8+PHjyWGqa6uDtPT01KbYrEIx3HYcQsEAigWi+jt7eU2tm1jaGhIYhooJU/QNA0HDhxgRyibzXoY4Fwux8+ndwMyizUyMoKXX36Z73nw4AE7LDR3APDSSy9xG8Mw0NfXx20ePXokGUmg7JjG43GPwifouo6ZmRmJzTcMQ1K8pmmivr5eMtCFQsGj8NwlAKurq5x1oHfReFD/FxcXpXvIuaV3keIRDRhQVmzEptDz2tvbpTaUkQHALIzbEIhzWSqVUCwWWRHW1tbi0aNHkkETGRmgPL4rKyuS429ZFuLxOMu3vr7OJZKiwaXnEdzy67ruYWn37NnDin99fR26rnvmobGxUXpPR0eHJxjajN2anZ2VrmtqajzzQ/JMTU3xGNG7aH5v3Lgh9dHtELhZz1wu5zFc9fX1kiNvWRZfi33J5/PS88LhsCdoSqVSUqCXSCSkTEtzczMMw5DGLBgM8rqiz0RnqKqqCqurq3zP06dPEQwGJecyEol4gsA33niD5c/n8/D5fCwvBaSHDx9m+Yi9npiY4HEAgBdffJGNuK7rHsJA0zREIhEsLy9Lge6dO3c8TK9hGPD5fKzDKGNAMgHA/fv3pfdv27YNlmVtqhNIN1NmorKykp+TyWTYSaX5dBwHk5OTvLZs20YgEOC1R2OazWb5Hk3TMDY2xraB7hGJAMoOiHuorq6O+7KxscEOEOmuUqkEx3FYX2azWYmcy+VyWF9f57Jnkr+yspIdJ3K+Kfgi+egeCvyDwSBWVlakOZufn5cIt+eff57npVgsIhAIsE4EyvtRzLK6iSfaW0tLSxz85PN5DkQo2BbnUdwbo6OjCAaDrPeAZ6Qbzen6+jqam5t5DoCy7iC5gfK+3LFjB5/vJRKgWCyioqICCwsLqKqqYh20sbGBbdu2MQteU1ODhYUFHifTNFEqlVBVVcVjXFVVhenpaSaafD4f+yOi/fP5fDh06BDcIJ1eWVmJbDYrBXy//vor30+YmpriAJZk7ujo4H4XCgVYluXZm3V1dZL/Eo/H8cILL/B1ZWUlOjs7JSLKTQC3trZ6Khp6enqkMRcdeVpPFLAB4EBXJL/W1tZw4MAB7mehUMDg4CDfY5omnjx5IhETpmlicHCQ52dychK6rqO/v5/b2LYtHRMwTROtra2SHiJ7LI7xyZMn2VY9efIEAJhwJ4TDYfz555/SM9wVIK+//jqva8dxoOu6J9P02muv8d8NDQ3Y2Nhgv2F0dBS2bePjjz+WsoWkS6hP09PTeOutt2AYBoCy/vjkk09w7NgxXtOnT5/G/v37cfToUbz55pv4+++/8d5776GyshLxeBxjY2P45ptv0N/fj8XFRfzxxx/4N7GlAp2xsTF8+umnOH78OA4fPoxsNos7d+7w9crKCrLZLI4fP449e/ZwRmR8fByDg4O4fv06gsEgMpkMuru7kUqlmB0CyoHM9evXWVkkEgmcPXuWHYMbN26gu7sb165dY5kSiQTOnDnD1zt37kRzc7PkqDQ2NjIzDpSVQiqVkhZxLBbjgAUoK1nxnrGxMbS2tiKdTvP/wuEwdF1nZba8vAxd13Hr1i1uo+s6zpw5w31yHAemaSKVSkltrl27xkooHA6zASV0dnZKKVjHcVBXV8fKN51Oo6enh4NCoKw0Lcvi8bVtG/v27cPIyAi3CYVCGBsb4+fatg1N0yQHkdKiooMvZqBIYYjGHJCd3nA47GHBAEhKFXjGjIlsUDab5T7QWFNQ5XZIRdC6ISXsdsIXFxf5ufQ+cX6BspKmsaD+uksA3Kl4YtOAZ8GRaPipDclOBwrd5Vrr6+vSOFBQIkIsO9R13SO/W+n7fD4MDw/zPTTem53LEFm6zTKQjuN4ytLE4BYorw2RRQPKjCvwLFgnR4LaA2DmieRyrxsAbNjpMzEr5vP58O6770pG0OfzseEXWejOzk6pdLNQKPBaF3+LTLfoKALljJO75n1kZERinyn4oTVvmqbkWJZKJeTzeYlpNwxDGmO/349bt27xWqyurmanX2xz+fJlTwZbnOeKigo8fvyY12AkEsHq6qrEmtLec2dCxS9bENeeuG/r6uo840Ey0p67cOHCptlJMfA0DIPPcYplqxsbG4hEIiy/pmno6emRApKnT59ylYGu67AsC7W1tbzPbNtGLBZDV1eXdE9zczO/v1gsor29XXKKMpmMRCjYto329nZ+rqZp6O3tZd1M2VuxREXTNI8j6vP5ODCj8tLOzk5Jd9C1bducaRKZdsdx0N7ezu/0+Xx4+PAhf+73+2GaJq/BYDAIv9+P27dvc//FcaYyOzELRl82RG0sy0JVVZW0R8XMUE1NDXK5nJT9onO5hFKpxEQc6YW1tTWpb6VSiW0XZWJM0+T10NbWhmKxyPf39vZK6zOTyTDbL2bTi8UizwPpXFE305oSbVVfXx+uXLnC1++88w76+vo46LQsC8lkUpo7sgmiXQ+FQtI4vPLKK5ibm5NIpBMnTkjBLFAmAEUi9MMPP5QyP/F4HKOjo5L+C4fDkl13HMdTqnvv3j1JnunpaU8wdPPmTf7b7/cjHo9z30qlEnp6eiTnPZlMYnh4mK8zmQxqamoke9zf34/h4WFpbD766CMOPgDg0KFDUrVMJBLx2K6jR49K63D37t24dOkS6xiyWWKbaDSKfD7P+zcSiSASiXjs8dmzZ/ldlCF0V3dcvXqV30V6QyShjhw5gq+++orHOJfLYd++ffyM+fl5DA0N4fvvvwdQ1iUnT57ElStXcPnyZQDAxMQENE3D9evX8dtvv+Hnn39GLBZDIBBAQ0MDlpeXkUgkUFVVBcMwsG/fPty7dw//JrZMoJNOp/Hll1/igw8+wMGDB5FOp/HTTz8hFAqhq6sLi4uLuHr1KgKBABKJBL744gsuizpx4gTOnz8PXddRKBQQDAYxMTGBtrY2NqbRaBQTExPsVAPlCe3v72cHOhqNIpVKSVH0w4cPPZmPS5cu8TVlYoBnhvbx48eS4xYOh/HkyRM2HkBZkYgsSnV1Nebm5qSSDMMwpDRpdXU1M1si+1tdXc2bhp5Jn9PZIfqCBuoDKWag7GRNTk56DtQODAwwsxeNRjE/Py+NTalUQnNzMyvoSCSC4eFhqU0+n5dK9qhWmt5NipjYFEIsFpOcDMAbtIjI5/MwTZP76D7zQKDaWWLs6B30mwIHMkyionYHAaJxBsAOIckbiUSY4RFZIkA+h0DvIhnI6IhlOqICjkajHDSTkyqyxADQ1dUlObBi+QShtraWn0NzREqUDqiLY14qlSQDWVtbC8uyJGVsWRZaWlr4OcRgu+ULhUJsDBzHYUeI+kSBmjvQpEBL7LfIggHPStdojN01/QA8JXHusQkGg1KmaLPzJt9++61k0ERHnMoHadyoXS6Xk8o0ad9aliUZS7EMjsZLJEaIoRf3COk/sY3oCFEb91gYhsFrmeSgec/n8wgEAqwjiakWS3xoL9Pc1NfXY2NjA9lsltfw2toal0oA5f1tmiaKxaKnxp7mYvv27TwGxEJT26WlJc8c0nxREEu6R3w+le6Qc2lZFme4Rdab7qc97/f7EQqFpPF87rnnmCEmFt4wDOkMxt69ezE+Ps73JBIJzM3NSQSDeE1txLIaymiL5SjhcFiqwxcDFmKKZ2ZmpPIrd4YgmUxidnbWkwEQy93i8bhnb4jlPLZtS2daRTtDn4uBpVimR4eeaY5E4oPsIK0PUQ/RWNNz0uk0KioqpP0iZm4IRBwSIpEIcrkcrwVxPRGxpOs6r4elpSXJERVLm0he8SwiZfwcx+Fsuvt8rWjfRdt78+ZNySmfm5vDjRs3pG+OHR8fl9Y26QeSN5lMegim33//nfcw4dy5cx6ip6mpSQp+Pv/8c2l8ZmZmpMwoUNaZlCWgvrqz7ZtlxclGEsR1bZomDh48yM/Ztm0bHj16JJFXk5OTUmZm7969UsUAANy6dQu7du3iPlVXV+Prr7/mPdXa2srf+EswDEPySeLxOJPeNGd3795FKBTicWhoaMD6+rrkA6XTaS73BYDu7m5Jl1NJm7s8U9M0HotoNIqNjQ0Ui0U+60NHKqhPu3btwsWLF7k6Q9M0xGIxqQy7v78fv/zyC5+d7urqwg8//IC7d++iu7sbAHDq1Cl0d3ejvr4ePp8Phw8fxurqKuLxOHbu3Im1tTWsrq5ibm6Of3bs2IF/E5qzWTHx/0N89913uHDhAiuElZUVFAoFNDQ0YGVlhVnpWCyGdDrNjBSl8UWIyvSf2vxv4K4b/0/Df0e+//Q+KCgo/DPc5zkU/udQOlBBQUFhc/j9fjQ2NmJhYYEz70QQ19bWoqOjAw8ePEChUEB9fT0GBgZw4sQJDv6OHTuGH3/88f9Upi0T6CgoKCgoKCgoKCgoKBC2TOmagoKCgoKCgoKCgoICQQU6CgoKCgoKCgoKCgpbDirQUVBQUFBQUFBQUFDYclCBjoKCgoKCgoKCgoLCloMKdBQUFBQUFBQUFBQUthxUoKOgoKCgoKCgoKCgsOWgAh0FBQUFBQUFBQUFhS0HFegoKCgoKCgoKCgoKGw5/Bf063wW7RBnuQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "\n",
    "#plt.style.use('ggplot')\n",
    "plt.plot(zgpa_df['trade_date'], zgpa_df['open'], \"g\")\n",
    "plt.grid(axis=\"y\")\n",
    "#plt.plot(ma.index, ma, \"b\");\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6efec2fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "160  601318.SH   20210701  64.26  65.17  63.58  64.76      64.28    0.48   \n",
      "159  601318.SH   20210702  64.03  64.07  62.10  62.30      64.76   -2.46   \n",
      "158  601318.SH   20210705  61.99  62.28  61.13  61.91      62.30   -0.39   \n",
      "157  601318.SH   20210706  62.42  63.14  61.85  62.90      61.91    0.99   \n",
      "156  601318.SH   20210707  62.69  63.41  62.09  62.37      62.90   -0.53   \n",
      "\n",
      "     pct_chg         vol      amount  \n",
      "160   0.7467  6.9262e+05  4.4492e+06  \n",
      "159  -3.7986  1.0281e+06  6.4647e+06  \n",
      "158  -0.6260  7.5065e+05  4.6293e+06  \n",
      "157   1.5991  6.8581e+05  4.2967e+06  \n",
      "156  -0.8426  5.2317e+05  3.2721e+06  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>64.03</td>\n",
       "      <td>64.07</td>\n",
       "      <td>62.10</td>\n",
       "      <td>62.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>61.99</td>\n",
       "      <td>62.28</td>\n",
       "      <td>61.13</td>\n",
       "      <td>61.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>62.42</td>\n",
       "      <td>63.14</td>\n",
       "      <td>61.85</td>\n",
       "      <td>62.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>62.69</td>\n",
       "      <td>63.41</td>\n",
       "      <td>62.09</td>\n",
       "      <td>62.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      open   high    low  close\n",
       "159  64.03  64.07  62.10  62.30\n",
       "158  61.99  62.28  61.13  61.91\n",
       "157  62.42  63.14  61.85  62.90\n",
       "156  62.69  63.41  62.09  62.37"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(zgpa_df.head())\n",
    "zgpa_df.iloc[1:5,2:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4211de95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "160    64.76\n",
      "159    62.30\n",
      "158    61.91\n",
      "Name: close, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>64.76</td>\n",
       "      <td>65.17</td>\n",
       "      <td>63.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>62.30</td>\n",
       "      <td>64.07</td>\n",
       "      <td>62.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>61.91</td>\n",
       "      <td>62.28</td>\n",
       "      <td>61.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     close   high    low\n",
       "160  64.76  65.17  63.58\n",
       "159  62.30  64.07  62.10\n",
       "158  61.91  62.28  61.13"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(zgpa_df.close[0:3])\n",
    "zgpa_df[['close','high','low']][0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "ef4c5f48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              ts_code  trade_date      open      high       low     close  \\\n",
      "date                                                                        \n",
      "2021-10-21  601318.SH    20211021 51.550000 54.880000 51.510000 54.280000   \n",
      "2021-10-08  601318.SH    20211008 50.500000 52.100000 50.110000 52.100000   \n",
      "2021-07-26  601318.SH    20210726 57.090000 57.090000 54.330000 54.500000   \n",
      "\n",
      "            pre_close    change   pct_chg            vol          amount  \\\n",
      "date                                                                       \n",
      "2021-10-21  51.370000  2.910000  5.664800 2110058.330000 11318517.616000   \n",
      "2021-10-08  48.360000  3.740000  7.733700 2180028.230000 11199501.310000   \n",
      "2021-07-26  57.640000 -3.140000 -5.447600 1174864.670000  6496203.840000   \n",
      "\n",
      "                 date  date_week  netChangeRatio  \n",
      "date                                              \n",
      "2021-10-21 2021-10-21          3        0.056648  \n",
      "2021-10-08 2021-10-08          4        0.077337  \n",
      "2021-07-26 2021-07-26          0       -0.054476  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ts_code                     601318.SH\n",
       "trade_date                   20211011\n",
       "open                        52.100000\n",
       "high                        53.390000\n",
       "low                         51.940000\n",
       "close                       52.740000\n",
       "pre_close                   52.100000\n",
       "change                       0.640000\n",
       "pct_chg                      1.228400\n",
       "vol                    1504499.540000\n",
       "amount                 7944560.509000\n",
       "date              2021-10-11 00:00:00\n",
       "date_week                           0\n",
       "netChangeRatio               0.012284\n",
       "Name: 2021-10-11 00:00:00, dtype: object"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pct_chg is netChangeRatio，命名差异而已\n",
    "zgpa_df['netChangeRatio']=np.true_divide(zgpa_df['change'], zgpa_df['pre_close'])\n",
    "\n",
    "print(zgpa_df[np.abs(zgpa_df['netChangeRatio'])>0.05])\n",
    "n=np.abs(zgpa_df['netChangeRatio'][zgpa_df['netChangeRatio']!=0]).argmax()\n",
    "zgpa_df.iloc[n]\n",
    "#np.abs(zgpa_df['netChangeRatio'][zgpa_df['netChangeRatio']!=0]).max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6635fa42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>netChangeRatio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20211008</td>\n",
       "      <td>50.50</td>\n",
       "      <td>52.10</td>\n",
       "      <td>50.11</td>\n",
       "      <td>52.10</td>\n",
       "      <td>48.36</td>\n",
       "      <td>3.74</td>\n",
       "      <td>7.7337</td>\n",
       "      <td>2.1800e+06</td>\n",
       "      <td>1.1200e+07</td>\n",
       "      <td>0.0773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20211021</td>\n",
       "      <td>51.55</td>\n",
       "      <td>54.88</td>\n",
       "      <td>51.51</td>\n",
       "      <td>54.28</td>\n",
       "      <td>51.37</td>\n",
       "      <td>2.91</td>\n",
       "      <td>5.6648</td>\n",
       "      <td>2.1101e+06</td>\n",
       "      <td>1.1319e+07</td>\n",
       "      <td>0.0566</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
       "96  601318.SH   20211008  50.50  52.10  50.11  52.10      48.36    3.74   \n",
       "87  601318.SH   20211021  51.55  54.88  51.51  54.28      51.37    2.91   \n",
       "\n",
       "    pct_chg         vol      amount  netChangeRatio  \n",
       "96   7.7337  2.1800e+06  1.1200e+07          0.0773  \n",
       "87   5.6648  2.1101e+06  1.1319e+07          0.0566  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zgpa_df[np.abs(zgpa_df['netChangeRatio']>0.02)&(zgpa_df.vol>2.5*zgpa_df.vol.mean())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "dcafc098",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>netChangeRatio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20210927</td>\n",
       "      <td>47.90</td>\n",
       "      <td>49.24</td>\n",
       "      <td>47.82</td>\n",
       "      <td>49.00</td>\n",
       "      <td>48.08</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1.9135</td>\n",
       "      <td>9.9008e+05</td>\n",
       "      <td>4.8125e+06</td>\n",
       "      <td>0.0191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20210928</td>\n",
       "      <td>48.99</td>\n",
       "      <td>49.63</td>\n",
       "      <td>48.52</td>\n",
       "      <td>49.26</td>\n",
       "      <td>49.00</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.5306</td>\n",
       "      <td>7.7951e+05</td>\n",
       "      <td>3.8321e+06</td>\n",
       "      <td>0.0053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20210929</td>\n",
       "      <td>48.77</td>\n",
       "      <td>49.54</td>\n",
       "      <td>48.52</td>\n",
       "      <td>49.19</td>\n",
       "      <td>49.26</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.1421</td>\n",
       "      <td>6.3130e+05</td>\n",
       "      <td>3.0951e+06</td>\n",
       "      <td>-0.0014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20210930</td>\n",
       "      <td>49.30</td>\n",
       "      <td>49.30</td>\n",
       "      <td>48.23</td>\n",
       "      <td>48.36</td>\n",
       "      <td>49.19</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>-1.6873</td>\n",
       "      <td>6.8007e+05</td>\n",
       "      <td>3.3005e+06</td>\n",
       "      <td>-0.0169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20211008</td>\n",
       "      <td>50.50</td>\n",
       "      <td>52.10</td>\n",
       "      <td>50.11</td>\n",
       "      <td>52.10</td>\n",
       "      <td>48.36</td>\n",
       "      <td>3.74</td>\n",
       "      <td>7.7337</td>\n",
       "      <td>2.1800e+06</td>\n",
       "      <td>1.1200e+07</td>\n",
       "      <td>0.0773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20211011</td>\n",
       "      <td>52.10</td>\n",
       "      <td>53.39</td>\n",
       "      <td>51.94</td>\n",
       "      <td>52.74</td>\n",
       "      <td>52.10</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.2284</td>\n",
       "      <td>1.5045e+06</td>\n",
       "      <td>7.9446e+06</td>\n",
       "      <td>0.0123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20211012</td>\n",
       "      <td>52.05</td>\n",
       "      <td>53.04</td>\n",
       "      <td>51.68</td>\n",
       "      <td>52.26</td>\n",
       "      <td>52.74</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.9101</td>\n",
       "      <td>9.4466e+05</td>\n",
       "      <td>4.9394e+06</td>\n",
       "      <td>-0.0091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20211013</td>\n",
       "      <td>52.10</td>\n",
       "      <td>52.19</td>\n",
       "      <td>50.90</td>\n",
       "      <td>51.75</td>\n",
       "      <td>52.26</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>-0.9759</td>\n",
       "      <td>6.4989e+05</td>\n",
       "      <td>3.3460e+06</td>\n",
       "      <td>-0.0098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20211014</td>\n",
       "      <td>51.70</td>\n",
       "      <td>52.00</td>\n",
       "      <td>51.20</td>\n",
       "      <td>51.41</td>\n",
       "      <td>51.75</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.6570</td>\n",
       "      <td>4.2701e+05</td>\n",
       "      <td>2.1982e+06</td>\n",
       "      <td>-0.0066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20211015</td>\n",
       "      <td>51.13</td>\n",
       "      <td>51.58</td>\n",
       "      <td>50.62</td>\n",
       "      <td>50.88</td>\n",
       "      <td>51.41</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-1.0309</td>\n",
       "      <td>7.3302e+05</td>\n",
       "      <td>3.7383e+06</td>\n",
       "      <td>-0.0103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20211018</td>\n",
       "      <td>51.08</td>\n",
       "      <td>51.24</td>\n",
       "      <td>49.68</td>\n",
       "      <td>49.86</td>\n",
       "      <td>50.88</td>\n",
       "      <td>-1.02</td>\n",
       "      <td>-2.0047</td>\n",
       "      <td>7.7197e+05</td>\n",
       "      <td>3.8627e+06</td>\n",
       "      <td>-0.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20211019</td>\n",
       "      <td>49.85</td>\n",
       "      <td>51.85</td>\n",
       "      <td>49.70</td>\n",
       "      <td>51.45</td>\n",
       "      <td>49.86</td>\n",
       "      <td>1.59</td>\n",
       "      <td>3.1889</td>\n",
       "      <td>8.4753e+05</td>\n",
       "      <td>4.3329e+06</td>\n",
       "      <td>0.0319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20211020</td>\n",
       "      <td>51.70</td>\n",
       "      <td>52.04</td>\n",
       "      <td>50.79</td>\n",
       "      <td>51.37</td>\n",
       "      <td>51.45</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.1555</td>\n",
       "      <td>6.6824e+05</td>\n",
       "      <td>3.4295e+06</td>\n",
       "      <td>-0.0016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20211021</td>\n",
       "      <td>51.55</td>\n",
       "      <td>54.88</td>\n",
       "      <td>51.51</td>\n",
       "      <td>54.28</td>\n",
       "      <td>51.37</td>\n",
       "      <td>2.91</td>\n",
       "      <td>5.6648</td>\n",
       "      <td>2.1101e+06</td>\n",
       "      <td>1.1319e+07</td>\n",
       "      <td>0.0566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20211022</td>\n",
       "      <td>54.68</td>\n",
       "      <td>54.85</td>\n",
       "      <td>53.60</td>\n",
       "      <td>54.29</td>\n",
       "      <td>54.28</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0184</td>\n",
       "      <td>1.0263e+06</td>\n",
       "      <td>5.5591e+06</td>\n",
       "      <td>0.0002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
       "100  601318.SH   20210927  47.90  49.24  47.82  49.00      48.08    0.92   \n",
       "99   601318.SH   20210928  48.99  49.63  48.52  49.26      49.00    0.26   \n",
       "98   601318.SH   20210929  48.77  49.54  48.52  49.19      49.26   -0.07   \n",
       "97   601318.SH   20210930  49.30  49.30  48.23  48.36      49.19   -0.83   \n",
       "96   601318.SH   20211008  50.50  52.10  50.11  52.10      48.36    3.74   \n",
       "95   601318.SH   20211011  52.10  53.39  51.94  52.74      52.10    0.64   \n",
       "94   601318.SH   20211012  52.05  53.04  51.68  52.26      52.74   -0.48   \n",
       "93   601318.SH   20211013  52.10  52.19  50.90  51.75      52.26   -0.51   \n",
       "92   601318.SH   20211014  51.70  52.00  51.20  51.41      51.75   -0.34   \n",
       "91   601318.SH   20211015  51.13  51.58  50.62  50.88      51.41   -0.53   \n",
       "90   601318.SH   20211018  51.08  51.24  49.68  49.86      50.88   -1.02   \n",
       "89   601318.SH   20211019  49.85  51.85  49.70  51.45      49.86    1.59   \n",
       "88   601318.SH   20211020  51.70  52.04  50.79  51.37      51.45   -0.08   \n",
       "87   601318.SH   20211021  51.55  54.88  51.51  54.28      51.37    2.91   \n",
       "86   601318.SH   20211022  54.68  54.85  53.60  54.29      54.28    0.01   \n",
       "\n",
       "     pct_chg         vol      amount  netChangeRatio  \n",
       "100   1.9135  9.9008e+05  4.8125e+06          0.0191  \n",
       "99    0.5306  7.7951e+05  3.8321e+06          0.0053  \n",
       "98   -0.1421  6.3130e+05  3.0951e+06         -0.0014  \n",
       "97   -1.6873  6.8007e+05  3.3005e+06         -0.0169  \n",
       "96    7.7337  2.1800e+06  1.1200e+07          0.0773  \n",
       "95    1.2284  1.5045e+06  7.9446e+06          0.0123  \n",
       "94   -0.9101  9.4466e+05  4.9394e+06         -0.0091  \n",
       "93   -0.9759  6.4989e+05  3.3460e+06         -0.0098  \n",
       "92   -0.6570  4.2701e+05  2.1982e+06         -0.0066  \n",
       "91   -1.0309  7.3302e+05  3.7383e+06         -0.0103  \n",
       "90   -2.0047  7.7197e+05  3.8627e+06         -0.0200  \n",
       "89    3.1889  8.4753e+05  4.3329e+06          0.0319  \n",
       "88   -0.1555  6.6824e+05  3.4295e+06         -0.0016  \n",
       "87    5.6648  2.1101e+06  1.1319e+07          0.0566  \n",
       "86    0.0184  1.0263e+06  5.5591e+06          0.0002  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zgpa_df[60:75]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "3ea1ee85",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
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       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>netChangeRatio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20210726</td>\n",
       "      <td>57.09</td>\n",
       "      <td>57.09</td>\n",
       "      <td>54.33</td>\n",
       "      <td>54.50</td>\n",
       "      <td>57.64</td>\n",
       "      <td>-3.14</td>\n",
       "      <td>-5.4476</td>\n",
       "      <td>1.1749e+06</td>\n",
       "      <td>6.4962e+06</td>\n",
       "      <td>-0.0545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20210819</td>\n",
       "      <td>55.59</td>\n",
       "      <td>55.76</td>\n",
       "      <td>53.66</td>\n",
       "      <td>53.71</td>\n",
       "      <td>56.04</td>\n",
       "      <td>-2.33</td>\n",
       "      <td>-4.1577</td>\n",
       "      <td>1.1486e+06</td>\n",
       "      <td>6.2311e+06</td>\n",
       "      <td>-0.0416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20210702</td>\n",
       "      <td>64.03</td>\n",
       "      <td>64.07</td>\n",
       "      <td>62.10</td>\n",
       "      <td>62.30</td>\n",
       "      <td>64.76</td>\n",
       "      <td>-2.46</td>\n",
       "      <td>-3.7986</td>\n",
       "      <td>1.0281e+06</td>\n",
       "      <td>6.4647e+06</td>\n",
       "      <td>-0.0380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20220125</td>\n",
       "      <td>52.60</td>\n",
       "      <td>52.90</td>\n",
       "      <td>51.10</td>\n",
       "      <td>51.20</td>\n",
       "      <td>53.13</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>-3.6326</td>\n",
       "      <td>7.7666e+05</td>\n",
       "      <td>4.0321e+06</td>\n",
       "      <td>-0.0363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20211029</td>\n",
       "      <td>50.82</td>\n",
       "      <td>51.23</td>\n",
       "      <td>49.24</td>\n",
       "      <td>49.57</td>\n",
       "      <td>51.30</td>\n",
       "      <td>-1.73</td>\n",
       "      <td>-3.3723</td>\n",
       "      <td>1.4666e+06</td>\n",
       "      <td>7.3175e+06</td>\n",
       "      <td>-0.0337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
       "143  601318.SH   20210726  57.09  57.09  54.33  54.50      57.64   -3.14   \n",
       "125  601318.SH   20210819  55.59  55.76  53.66  53.71      56.04   -2.33   \n",
       "159  601318.SH   20210702  64.03  64.07  62.10  62.30      64.76   -2.46   \n",
       "20   601318.SH   20220125  52.60  52.90  51.10  51.20      53.13   -1.93   \n",
       "81   601318.SH   20211029  50.82  51.23  49.24  49.57      51.30   -1.73   \n",
       "\n",
       "     pct_chg         vol      amount  netChangeRatio  \n",
       "143  -5.4476  1.1749e+06  6.4962e+06         -0.0545  \n",
       "125  -4.1577  1.1486e+06  6.2311e+06         -0.0416  \n",
       "159  -3.7986  1.0281e+06  6.4647e+06         -0.0380  \n",
       "20   -3.6326  7.7666e+05  4.0321e+06         -0.0363  \n",
       "81   -3.3723  1.4666e+06  7.3175e+06         -0.0337  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort_index是针对索引列的\n",
    "zgpa_df.sort_values(by='netChangeRatio')[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "12aa0f79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>netChangeRatio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20211008</td>\n",
       "      <td>50.50</td>\n",
       "      <td>52.10</td>\n",
       "      <td>50.11</td>\n",
       "      <td>52.10</td>\n",
       "      <td>48.36</td>\n",
       "      <td>3.74</td>\n",
       "      <td>7.7337</td>\n",
       "      <td>2.1800e+06</td>\n",
       "      <td>1.1200e+07</td>\n",
       "      <td>0.0773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20211021</td>\n",
       "      <td>51.55</td>\n",
       "      <td>54.88</td>\n",
       "      <td>51.51</td>\n",
       "      <td>54.28</td>\n",
       "      <td>51.37</td>\n",
       "      <td>2.91</td>\n",
       "      <td>5.6648</td>\n",
       "      <td>2.1101e+06</td>\n",
       "      <td>1.1319e+07</td>\n",
       "      <td>0.0566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20211111</td>\n",
       "      <td>49.11</td>\n",
       "      <td>51.16</td>\n",
       "      <td>49.00</td>\n",
       "      <td>51.15</td>\n",
       "      <td>49.13</td>\n",
       "      <td>2.02</td>\n",
       "      <td>4.1115</td>\n",
       "      <td>1.1109e+06</td>\n",
       "      <td>5.5997e+06</td>\n",
       "      <td>0.0411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20220207</td>\n",
       "      <td>51.34</td>\n",
       "      <td>52.47</td>\n",
       "      <td>50.70</td>\n",
       "      <td>51.98</td>\n",
       "      <td>49.97</td>\n",
       "      <td>2.01</td>\n",
       "      <td>4.0224</td>\n",
       "      <td>9.4042e+05</td>\n",
       "      <td>4.8588e+06</td>\n",
       "      <td>0.0402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20220120</td>\n",
       "      <td>51.50</td>\n",
       "      <td>53.52</td>\n",
       "      <td>51.45</td>\n",
       "      <td>53.40</td>\n",
       "      <td>51.45</td>\n",
       "      <td>1.95</td>\n",
       "      <td>3.7901</td>\n",
       "      <td>1.4484e+06</td>\n",
       "      <td>7.6618e+06</td>\n",
       "      <td>0.0379</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
       "96  601318.SH   20211008  50.50  52.10  50.11  52.10      48.36    3.74   \n",
       "87  601318.SH   20211021  51.55  54.88  51.51  54.28      51.37    2.91   \n",
       "72  601318.SH   20211111  49.11  51.16  49.00  51.15      49.13    2.02   \n",
       "16  601318.SH   20220207  51.34  52.47  50.70  51.98      49.97    2.01   \n",
       "23  601318.SH   20220120  51.50  53.52  51.45  53.40      51.45    1.95   \n",
       "\n",
       "    pct_chg         vol      amount  netChangeRatio  \n",
       "96   7.7337  2.1800e+06  1.1200e+07          0.0773  \n",
       "87   5.6648  2.1101e+06  1.1319e+07          0.0566  \n",
       "72   4.1115  1.1109e+06  5.5997e+06          0.0411  \n",
       "16   4.0224  9.4042e+05  4.8588e+06          0.0402  \n",
       "23   3.7901  1.4484e+06  7.6618e+06          0.0379  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zgpa_df.sort_values(by='netChangeRatio', ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "791f117b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(zgpa_df[['vol']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "bf512e3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 161 entries, 160 to 0\n",
      "Data columns (total 12 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   ts_code         161 non-null    object \n",
      " 1   trade_date      161 non-null    object \n",
      " 2   open            161 non-null    float64\n",
      " 3   high            161 non-null    float64\n",
      " 4   low             161 non-null    float64\n",
      " 5   close           161 non-null    float64\n",
      " 6   pre_close       161 non-null    float64\n",
      " 7   change          161 non-null    float64\n",
      " 8   pct_chg         161 non-null    float64\n",
      " 9   vol             161 non-null    float64\n",
      " 10  amount          161 non-null    float64\n",
      " 11  netChangeRatio  161 non-null    float64\n",
      "dtypes: float64(10), object(2)\n",
      "memory usage: 20.4+ KB\n"
     ]
    }
   ],
   "source": [
    "zgpa_df.info()\n",
    "#zgpa_df[['vol']].apply(lambda x:format(float(x),','))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "6a1d939c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>692617.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>1028081.620000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>750646.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>685812.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>523167.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>463601.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>975396.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>544416.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>731883.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>568035.790000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>161 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               vol\n",
       "160  692617.350000\n",
       "159 1028081.620000\n",
       "158  750646.750000\n",
       "157  685812.850000\n",
       "156  523167.540000\n",
       "..             ...\n",
       "4    463601.660000\n",
       "3    975396.020000\n",
       "2    544416.390000\n",
       "1    731883.260000\n",
       "0    568035.790000\n",
       "\n",
       "[161 rows x 1 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('float_format', '{:f}'.format)\n",
    "zgpa_df[['vol']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "3d97cca2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_abe5c_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Model:</th>\n",
       "      <th class=\"col_heading level0 col0\" colspan=\"2\">Decision Tree</th>\n",
       "      <th class=\"col_heading level0 col2\" colspan=\"2\">Regression</th>\n",
       "      <th class=\"col_heading level0 col4\" colspan=\"2\">Random</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level1\" >Predicted:</th>\n",
       "      <th class=\"col_heading level1 col0\" >Tumour</th>\n",
       "      <th class=\"col_heading level1 col1\" >Non-Tumour</th>\n",
       "      <th class=\"col_heading level1 col2\" >Tumour</th>\n",
       "      <th class=\"col_heading level1 col3\" >Non-Tumour</th>\n",
       "      <th class=\"col_heading level1 col4\" >Tumour</th>\n",
       "      <th class=\"col_heading level1 col5\" >Non-Tumour</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Actual Label:</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_abe5c_level0_row0\" class=\"row_heading level0 row0\" >Tumour (Positive)</th>\n",
       "      <td id=\"T_abe5c_row0_col0\" class=\"data row0 col0\" >38.0000</td>\n",
       "      <td id=\"T_abe5c_row0_col1\" class=\"data row0 col1\" >2.0000</td>\n",
       "      <td id=\"T_abe5c_row0_col2\" class=\"data row0 col2\" >18.0000</td>\n",
       "      <td id=\"T_abe5c_row0_col3\" class=\"data row0 col3\" >22.0000</td>\n",
       "      <td id=\"T_abe5c_row0_col4\" class=\"data row0 col4\" >21</td>\n",
       "      <td id=\"T_abe5c_row0_col5\" class=\"data row0 col5\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_abe5c_level0_row1\" class=\"row_heading level0 row1\" >Non-Tumour (Negative)</th>\n",
       "      <td id=\"T_abe5c_row1_col0\" class=\"data row1 col0\" >19.0000</td>\n",
       "      <td id=\"T_abe5c_row1_col1\" class=\"data row1 col1\" >439.0000</td>\n",
       "      <td id=\"T_abe5c_row1_col2\" class=\"data row1 col2\" >6.0000</td>\n",
       "      <td id=\"T_abe5c_row1_col3\" class=\"data row1 col3\" >452.0000</td>\n",
       "      <td id=\"T_abe5c_row1_col4\" class=\"data row1 col4\" >226</td>\n",
       "      <td id=\"T_abe5c_row1_col5\" class=\"data row1 col5\" >232.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x162c937f0>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "\n",
    "df = pd.DataFrame([[38.0, 2.0, 18.0, 22.0, 21, np.nan],[19, 439, 6, 452, 226,232]],\n",
    "                  index=pd.Index(['Tumour (Positive)', 'Non-Tumour (Negative)'], name='Actual Label:'),\n",
    "                  columns=pd.MultiIndex.from_product([['Decision Tree', 'Regression', 'Random'],['Tumour', 'Non-Tumour']], names=['Model:', 'Predicted:']))\n",
    "df.style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "20636127",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>netChangeRatio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20210701</td>\n",
       "      <td>64.260000</td>\n",
       "      <td>65.170000</td>\n",
       "      <td>63.580000</td>\n",
       "      <td>64.760000</td>\n",
       "      <td>64.280000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.746700</td>\n",
       "      <td>692617.350000</td>\n",
       "      <td>4449223.158000</td>\n",
       "      <td>0.007467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20210702</td>\n",
       "      <td>64.030000</td>\n",
       "      <td>64.070000</td>\n",
       "      <td>62.100000</td>\n",
       "      <td>62.300000</td>\n",
       "      <td>64.760000</td>\n",
       "      <td>-2.460000</td>\n",
       "      <td>-3.798600</td>\n",
       "      <td>1028081.620000</td>\n",
       "      <td>6464701.802000</td>\n",
       "      <td>-0.037986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20210705</td>\n",
       "      <td>61.990000</td>\n",
       "      <td>62.280000</td>\n",
       "      <td>61.130000</td>\n",
       "      <td>61.910000</td>\n",
       "      <td>62.300000</td>\n",
       "      <td>-0.390000</td>\n",
       "      <td>-0.626000</td>\n",
       "      <td>750646.750000</td>\n",
       "      <td>4629283.875000</td>\n",
       "      <td>-0.006260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20210706</td>\n",
       "      <td>62.420000</td>\n",
       "      <td>63.140000</td>\n",
       "      <td>61.850000</td>\n",
       "      <td>62.900000</td>\n",
       "      <td>61.910000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>1.599100</td>\n",
       "      <td>685812.850000</td>\n",
       "      <td>4296674.949000</td>\n",
       "      <td>0.015991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20210707</td>\n",
       "      <td>62.690000</td>\n",
       "      <td>63.410000</td>\n",
       "      <td>62.090000</td>\n",
       "      <td>62.370000</td>\n",
       "      <td>62.900000</td>\n",
       "      <td>-0.530000</td>\n",
       "      <td>-0.842600</td>\n",
       "      <td>523167.540000</td>\n",
       "      <td>3272128.188000</td>\n",
       "      <td>-0.008426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ts_code trade_date      open      high       low     close  pre_close  \\\n",
       "160  601318.SH   20210701 64.260000 65.170000 63.580000 64.760000  64.280000   \n",
       "159  601318.SH   20210702 64.030000 64.070000 62.100000 62.300000  64.760000   \n",
       "158  601318.SH   20210705 61.990000 62.280000 61.130000 61.910000  62.300000   \n",
       "157  601318.SH   20210706 62.420000 63.140000 61.850000 62.900000  61.910000   \n",
       "156  601318.SH   20210707 62.690000 63.410000 62.090000 62.370000  62.900000   \n",
       "\n",
       "       change   pct_chg            vol         amount  netChangeRatio  \n",
       "160  0.480000  0.746700  692617.350000 4449223.158000        0.007467  \n",
       "159 -2.460000 -3.798600 1028081.620000 6464701.802000       -0.037986  \n",
       "158 -0.390000 -0.626000  750646.750000 4629283.875000       -0.006260  \n",
       "157  0.990000  1.599100  685812.850000 4296674.949000        0.015991  \n",
       "156 -0.530000 -0.842600  523167.540000 3272128.188000       -0.008426  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zgpa_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "d304af7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'tableau-colorblind10']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    ">>> import numpy as np\n",
    ">>> import matplotlib.pyplot as plt\n",
    ">>> print(plt.style.available)\n",
    ">>> plt.style.use(['dark_background','seaborn-whitegrid','tableau-colorblind10'])\n",
    ">>> plt.plot(np.sin(np.linspace(0, 2 * np.pi)), 'r-o')\n",
    ">>>\n",
    ">>> # Some plotting code with the default style\n",
    ">>>\n",
    ">>> plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "55923ebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "160   64.760000\n",
       "159   62.300000\n",
       "158   61.910000\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zgpa_df.close[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "1929a9de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "160         NaN\n",
       "159   -0.037986\n",
       "158   -0.006260\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pct_change需要注意序列排序问题\n",
    "zgpa_df.close.pct_change()[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "96d7e2ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         NaN\n",
       "1   -0.012259\n",
       "2    0.012608\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zgpa_df.close.sort_index(ascending=True).pct_change()[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "4fc98743",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>netChangeRatio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20210701</td>\n",
       "      <td>64.260000</td>\n",
       "      <td>65.170000</td>\n",
       "      <td>63.580000</td>\n",
       "      <td>64.760000</td>\n",
       "      <td>64.280000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.746700</td>\n",
       "      <td>692617.350000</td>\n",
       "      <td>4449223.158000</td>\n",
       "      <td>0.007467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20210702</td>\n",
       "      <td>64.030000</td>\n",
       "      <td>64.070000</td>\n",
       "      <td>62.100000</td>\n",
       "      <td>62.300000</td>\n",
       "      <td>64.760000</td>\n",
       "      <td>-2.460000</td>\n",
       "      <td>-3.798600</td>\n",
       "      <td>1028081.620000</td>\n",
       "      <td>6464701.802000</td>\n",
       "      <td>-0.037986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20210705</td>\n",
       "      <td>61.990000</td>\n",
       "      <td>62.280000</td>\n",
       "      <td>61.130000</td>\n",
       "      <td>61.910000</td>\n",
       "      <td>62.300000</td>\n",
       "      <td>-0.390000</td>\n",
       "      <td>-0.626000</td>\n",
       "      <td>750646.750000</td>\n",
       "      <td>4629283.875000</td>\n",
       "      <td>-0.006260</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ts_code trade_date      open      high       low     close  pre_close  \\\n",
       "160  601318.SH   20210701 64.260000 65.170000 63.580000 64.760000  64.280000   \n",
       "159  601318.SH   20210702 64.030000 64.070000 62.100000 62.300000  64.760000   \n",
       "158  601318.SH   20210705 61.990000 62.280000 61.130000 61.910000  62.300000   \n",
       "\n",
       "       change   pct_chg            vol         amount  netChangeRatio  \n",
       "160  0.480000  0.746700  692617.350000 4449223.158000        0.007467  \n",
       "159 -2.460000 -3.798600 1028081.620000 6464701.802000       -0.037986  \n",
       "158 -0.390000 -0.626000  750646.750000 4629283.875000       -0.006260  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zgpa_df[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "21cfdd17",
   "metadata": {},
   "outputs": [],
   "source": [
    "#zgpa_df.close.pct_change(freq=\"5D\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "1a5350da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20220301</td>\n",
       "      <td>51.010000</td>\n",
       "      <td>51.400000</td>\n",
       "      <td>50.570000</td>\n",
       "      <td>51.390000</td>\n",
       "      <td>50.760000</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>1.241100</td>\n",
       "      <td>568035.790000</td>\n",
       "      <td>2895181.121000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20220228</td>\n",
       "      <td>51.100000</td>\n",
       "      <td>51.100000</td>\n",
       "      <td>50.250000</td>\n",
       "      <td>50.760000</td>\n",
       "      <td>51.400000</td>\n",
       "      <td>-0.640000</td>\n",
       "      <td>-1.245100</td>\n",
       "      <td>731883.260000</td>\n",
       "      <td>3707888.597000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20220225</td>\n",
       "      <td>51.700000</td>\n",
       "      <td>52.060000</td>\n",
       "      <td>51.210000</td>\n",
       "      <td>51.400000</td>\n",
       "      <td>51.460000</td>\n",
       "      <td>-0.060000</td>\n",
       "      <td>-0.116600</td>\n",
       "      <td>544416.390000</td>\n",
       "      <td>2808109.116000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20220224</td>\n",
       "      <td>52.150000</td>\n",
       "      <td>52.250000</td>\n",
       "      <td>51.090000</td>\n",
       "      <td>51.460000</td>\n",
       "      <td>52.890000</td>\n",
       "      <td>-1.430000</td>\n",
       "      <td>-2.703700</td>\n",
       "      <td>975396.020000</td>\n",
       "      <td>5039207.433000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20220223</td>\n",
       "      <td>53.100000</td>\n",
       "      <td>53.300000</td>\n",
       "      <td>52.690000</td>\n",
       "      <td>52.890000</td>\n",
       "      <td>53.070000</td>\n",
       "      <td>-0.180000</td>\n",
       "      <td>-0.339200</td>\n",
       "      <td>463601.660000</td>\n",
       "      <td>2449763.789000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ts_code  trade_date      open      high       low     close  pre_close  \\\n",
       "0  601318.SH    20220301 51.010000 51.400000 50.570000 51.390000  50.760000   \n",
       "1  601318.SH    20220228 51.100000 51.100000 50.250000 50.760000  51.400000   \n",
       "2  601318.SH    20220225 51.700000 52.060000 51.210000 51.400000  51.460000   \n",
       "3  601318.SH    20220224 52.150000 52.250000 51.090000 51.460000  52.890000   \n",
       "4  601318.SH    20220223 53.100000 53.300000 52.690000 52.890000  53.070000   \n",
       "\n",
       "     change   pct_chg           vol         amount  \n",
       "0  0.630000  1.241100 568035.790000 2895181.121000  \n",
       "1 -0.640000 -1.245100 731883.260000 3707888.597000  \n",
       "2 -0.060000 -0.116600 544416.390000 2808109.116000  \n",
       "3 -1.430000 -2.703700 975396.020000 5039207.433000  \n",
       "4 -0.180000 -0.339200 463601.660000 2449763.789000  "
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#zgpa_df.to_csv('./data/zhpa_df.csv', columns=zgpa_df.columns,index=True)\n",
    "zgpa_df_load=pd.read_csv('./data/zhpa_df.csv', parse_dates=True,index_col=0)\n",
    "zgpa_df=pd.read_csv('./data/zhpa_df.csv', parse_dates=True,index_col=0)\n",
    "\n",
    "# pct_chg is netChangeRatio，命名差异而已\n",
    "zgpa_df['netChangeRatio']=np.true_divide(zgpa_df['change'], zgpa_df['pre_close'])\n",
    "zgpa_df_load.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "307f5f0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "zgpa_df.netChangeRatio.hist(bins=120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "1f2c845f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.001, 0.00155]     17\n",
       "(0.00155, 0.00327]    16\n",
       "(0.00327, 0.00569]    16\n",
       "(0.00569, 0.00747]    16\n",
       "(0.00747, 0.00978]    16\n",
       "(0.00978, 0.0115]     16\n",
       "(0.0115, 0.015]       16\n",
       "(0.015, 0.02]         16\n",
       "(0.02, 0.0319]        16\n",
       "(0.0319, 0.0773]      16\n",
       "Name: netChangeRatio, dtype: int64"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats=pd.qcut(np.abs(zgpa_df.netChangeRatio),10)\n",
    "cats.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "70750edb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.01, 0.0]      51\n",
       "(0.0, 0.01]       32\n",
       "(-0.02, -0.01]    27\n",
       "(0.01, 0.02]      18\n",
       "(-0.03, -0.02]    10\n",
       "(0.03, 0.04]       7\n",
       "(-0.04, -0.03]     5\n",
       "(0.02, 0.03]       5\n",
       "(0.04, 0.05]       2\n",
       "(0.05, 0.06]       1\n",
       "(0.07, 0.08]       1\n",
       "(-0.06, -0.05]     1\n",
       "(-0.05, -0.04]     1\n",
       "(0.08, 0.09]       0\n",
       "(0.06, 0.07]       0\n",
       "(-0.1, -0.09]      0\n",
       "(-0.09, -0.08]     0\n",
       "(-0.07, -0.06]     0\n",
       "(-0.08, -0.07]     0\n",
       "(0.09, 0.1]        0\n",
       "Name: netChangeRatio, dtype: int64"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins=[ (float(x)-10)/100 for x in range(0,21)]\n",
    "#print(bins)\n",
    "cats=pd.cut(zgpa_df.netChangeRatio, bins)\n",
    "cats.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "25c87d50",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
       "<p>161 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cr_dummies_(-0.1, -0.09]  cr_dummies_(-0.09, -0.08]  \\\n",
       "0                           0                          0   \n",
       "1                           0                          0   \n",
       "2                           0                          0   \n",
       "3                           0                          0   \n",
       "4                           0                          0   \n",
       "..                        ...                        ...   \n",
       "156                         0                          0   \n",
       "157                         0                          0   \n",
       "158                         0                          0   \n",
       "159                         0                          0   \n",
       "160                         0                          0   \n",
       "\n",
       "     cr_dummies_(-0.08, -0.07]  cr_dummies_(-0.07, -0.06]  \\\n",
       "0                            0                          0   \n",
       "1                            0                          0   \n",
       "2                            0                          0   \n",
       "3                            0                          0   \n",
       "4                            0                          0   \n",
       "..                         ...                        ...   \n",
       "156                          0                          0   \n",
       "157                          0                          0   \n",
       "158                          0                          0   \n",
       "159                          0                          0   \n",
       "160                          0                          0   \n",
       "\n",
       "     cr_dummies_(-0.06, -0.05]  cr_dummies_(-0.05, -0.04]  \\\n",
       "0                            0                          0   \n",
       "1                            0                          0   \n",
       "2                            0                          0   \n",
       "3                            0                          0   \n",
       "4                            0                          0   \n",
       "..                         ...                        ...   \n",
       "156                          0                          0   \n",
       "157                          0                          0   \n",
       "158                          0                          0   \n",
       "159                          0                          0   \n",
       "160                          0                          0   \n",
       "\n",
       "     cr_dummies_(-0.04, -0.03]  cr_dummies_(-0.03, -0.02]  \\\n",
       "0                            0                          0   \n",
       "1                            0                          0   \n",
       "2                            0                          0   \n",
       "3                            0                          1   \n",
       "4                            0                          0   \n",
       "..                         ...                        ...   \n",
       "156                          0                          0   \n",
       "157                          0                          0   \n",
       "158                          0                          0   \n",
       "159                          1                          0   \n",
       "160                          0                          0   \n",
       "\n",
       "     cr_dummies_(-0.02, -0.01]  cr_dummies_(-0.01, 0.0]  \\\n",
       "0                            0                        0   \n",
       "1                            1                        0   \n",
       "2                            0                        1   \n",
       "3                            0                        0   \n",
       "4                            0                        1   \n",
       "..                         ...                      ...   \n",
       "156                          0                        1   \n",
       "157                          0                        0   \n",
       "158                          0                        1   \n",
       "159                          0                        0   \n",
       "160                          0                        0   \n",
       "\n",
       "     cr_dummies_(0.0, 0.01]  cr_dummies_(0.01, 0.02]  cr_dummies_(0.02, 0.03]  \\\n",
       "0                         0                        1                        0   \n",
       "1                         0                        0                        0   \n",
       "2                         0                        0                        0   \n",
       "3                         0                        0                        0   \n",
       "4                         0                        0                        0   \n",
       "..                      ...                      ...                      ...   \n",
       "156                       0                        0                        0   \n",
       "157                       0                        1                        0   \n",
       "158                       0                        0                        0   \n",
       "159                       0                        0                        0   \n",
       "160                       1                        0                        0   \n",
       "\n",
       "     cr_dummies_(0.03, 0.04]  cr_dummies_(0.04, 0.05]  \\\n",
       "0                          0                        0   \n",
       "1                          0                        0   \n",
       "2                          0                        0   \n",
       "3                          0                        0   \n",
       "4                          0                        0   \n",
       "..                       ...                      ...   \n",
       "156                        0                        0   \n",
       "157                        0                        0   \n",
       "158                        0                        0   \n",
       "159                        0                        0   \n",
       "160                        0                        0   \n",
       "\n",
       "     cr_dummies_(0.05, 0.06]  cr_dummies_(0.06, 0.07]  \\\n",
       "0                          0                        0   \n",
       "1                          0                        0   \n",
       "2                          0                        0   \n",
       "3                          0                        0   \n",
       "4                          0                        0   \n",
       "..                       ...                      ...   \n",
       "156                        0                        0   \n",
       "157                        0                        0   \n",
       "158                        0                        0   \n",
       "159                        0                        0   \n",
       "160                        0                        0   \n",
       "\n",
       "     cr_dummies_(0.07, 0.08]  cr_dummies_(0.08, 0.09]  cr_dummies_(0.09, 0.1]  \n",
       "0                          0                        0                       0  \n",
       "1                          0                        0                       0  \n",
       "2                          0                        0                       0  \n",
       "3                          0                        0                       0  \n",
       "4                          0                        0                       0  \n",
       "..                       ...                      ...                     ...  \n",
       "156                        0                        0                       0  \n",
       "157                        0                        0                       0  \n",
       "158                        0                        0                       0  \n",
       "159                        0                        0                       0  \n",
       "160                        0                        0                       0  \n",
       "\n",
       "[161 rows x 20 columns]"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "change_ration_dummies=pd.get_dummies(cats, prefix='cr_dummies')\n",
    "change_ration_dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "9dbb8ec7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
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       "      <th>amount</th>\n",
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       "      <th>positive</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20220301</td>\n",
       "      <td>51.010000</td>\n",
       "      <td>51.400000</td>\n",
       "      <td>50.570000</td>\n",
       "      <td>51.390000</td>\n",
       "      <td>50.760000</td>\n",
       "      <td>0.630000</td>\n",
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       "      <td>568035.790000</td>\n",
       "      <td>2895181.121000</td>\n",
       "      <td>0.012411</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20220228</td>\n",
       "      <td>51.100000</td>\n",
       "      <td>51.100000</td>\n",
       "      <td>50.250000</td>\n",
       "      <td>50.760000</td>\n",
       "      <td>51.400000</td>\n",
       "      <td>-0.640000</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20220225</td>\n",
       "      <td>51.700000</td>\n",
       "      <td>52.060000</td>\n",
       "      <td>51.210000</td>\n",
       "      <td>51.400000</td>\n",
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       "      <td>544416.390000</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20220224</td>\n",
       "      <td>52.150000</td>\n",
       "      <td>52.250000</td>\n",
       "      <td>51.090000</td>\n",
       "      <td>51.460000</td>\n",
       "      <td>52.890000</td>\n",
       "      <td>-1.430000</td>\n",
       "      <td>-2.703700</td>\n",
       "      <td>975396.020000</td>\n",
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       "      <td>-0.027037</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20220223</td>\n",
       "      <td>53.100000</td>\n",
       "      <td>53.300000</td>\n",
       "      <td>52.690000</td>\n",
       "      <td>52.890000</td>\n",
       "      <td>53.070000</td>\n",
       "      <td>-0.180000</td>\n",
       "      <td>-0.339200</td>\n",
       "      <td>463601.660000</td>\n",
       "      <td>2449763.789000</td>\n",
       "      <td>-0.003392</td>\n",
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      ],
      "text/plain": [
       "     ts_code  trade_date      open      high       low     close  pre_close  \\\n",
       "0  601318.SH    20220301 51.010000 51.400000 50.570000 51.390000  50.760000   \n",
       "1  601318.SH    20220228 51.100000 51.100000 50.250000 50.760000  51.400000   \n",
       "2  601318.SH    20220225 51.700000 52.060000 51.210000 51.400000  51.460000   \n",
       "3  601318.SH    20220224 52.150000 52.250000 51.090000 51.460000  52.890000   \n",
       "4  601318.SH    20220223 53.100000 53.300000 52.690000 52.890000  53.070000   \n",
       "\n",
       "     change   pct_chg           vol         amount  netChangeRatio  positive  \n",
       "0  0.630000  1.241100 568035.790000 2895181.121000        0.012411         1  \n",
       "1 -0.640000 -1.245100 731883.260000 3707888.597000       -0.012451         0  \n",
       "2 -0.060000 -0.116600 544416.390000 2808109.116000       -0.001166         0  \n",
       "3 -1.430000 -2.703700 975396.020000 5039207.433000       -0.027037         0  \n",
       "4 -0.180000 -0.339200 463601.660000 2449763.789000       -0.003392         0  "
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zgpa_df['positive']=np.where(zgpa_df.netChangeRatio>0,1,0)\n",
    "zgpa_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "91f1a78f",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>2021-07-05</td>\n",
       "      <td>20210705</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>2021-07-02</td>\n",
       "      <td>20210702</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>20210701</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>161 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  trade_date  date_week\n",
       "0   2022-03-01    20220301          1\n",
       "1   2022-02-28    20220228          0\n",
       "2   2022-02-25    20220225          4\n",
       "3   2022-02-24    20220224          3\n",
       "4   2022-02-23    20220223          2\n",
       "..         ...         ...        ...\n",
       "156 2021-07-07    20210707          2\n",
       "157 2021-07-06    20210706          1\n",
       "158 2021-07-05    20210705          0\n",
       "159 2021-07-02    20210702          4\n",
       "160 2021-07-01    20210701          3\n",
       "\n",
       "[161 rows x 3 columns]"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zgpa_df['date'] = zgpa_df['trade_date'].apply(lambda x: pd.to_datetime(str(x), format='%Y%m%d'))\n",
    "\n",
    "zgpa_df['date_week']=zgpa_df[\"date\"].dt.weekday\n",
    "zgpa_df[['date','trade_date','date_week']]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "0222639c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>positive</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_week</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "positive    0   1\n",
       "date_week        \n",
       "0          20  11\n",
       "1          18  14\n",
       "2          20  12\n",
       "3          17  16\n",
       "4          20  13"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xt=pd.crosstab(zgpa_df.date_week, zgpa_df.positive)\n",
    "xt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "4a5cf180",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>positive</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_week</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.645161</td>\n",
       "      <td>0.354839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.437500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.515152</td>\n",
       "      <td>0.484848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.606061</td>\n",
       "      <td>0.393939</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "positive         0        1\n",
       "date_week                  \n",
       "0         0.645161 0.354839\n",
       "1         0.562500 0.437500\n",
       "2         0.625000 0.375000\n",
       "3         0.515152 0.484848\n",
       "4         0.606061 0.393939"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xt_pct=xt.div(xt.sum(1).astype(float), axis=0)\n",
    "xt_pct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "e16836aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'positive')"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xt_pct.plot(figsize=(8,5), kind='bar', stacked=True, title='date_week -> positive')\n",
    "plt.xlabel('date_week')\n",
    "plt.ylabel('positive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "829f979e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            151, 152, 153, 154, 155, 156, 157, 158, 159, 160],\n",
       "           dtype='int64', length=161)"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#zgpa_df.pivot_table(['positive', columns=['date_week']])\n",
    "#groupby\n",
    "zgpa_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "f6965388",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_week  positive\n",
       "0          0           20\n",
       "           1           11\n",
       "1          0           18\n",
       "           1           14\n",
       "2          0           20\n",
       "           1           12\n",
       "3          0           17\n",
       "           1           16\n",
       "4          0           20\n",
       "           1           13\n",
       "Name: positive, dtype: int64"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zgpa_df.groupby(['date_week','positive'])['positive'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "eab2a3d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>positive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_week</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.354839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.437500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.484848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.393939</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           positive\n",
       "date_week          \n",
       "0          0.354839\n",
       "1          0.437500\n",
       "2          0.375000\n",
       "3          0.484848\n",
       "4          0.393939"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zgpa_df.set_index('date_week',append=True)\n",
    "zgpa_df.pivot_table(['positive'],index=['date_week'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "ff0284be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.489155\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 161 entries, 0 to 160\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count  Dtype         \n",
      "---  ------          --------------  -----         \n",
      " 0   ts_code         161 non-null    object        \n",
      " 1   trade_date      161 non-null    int64         \n",
      " 2   open            161 non-null    float64       \n",
      " 3   high            161 non-null    float64       \n",
      " 4   low             161 non-null    float64       \n",
      " 5   close           161 non-null    float64       \n",
      " 6   pre_close       161 non-null    float64       \n",
      " 7   change          161 non-null    float64       \n",
      " 8   pct_chg         161 non-null    float64       \n",
      " 9   vol             161 non-null    float64       \n",
      " 10  amount          161 non-null    float64       \n",
      " 11  netChangeRatio  161 non-null    float64       \n",
      " 12  positive        161 non-null    int64         \n",
      " 13  date            161 non-null    datetime64[ns]\n",
      " 14  date_week       161 non-null    int64         \n",
      "dtypes: datetime64[ns](1), float64(10), int64(3), object(1)\n",
      "memory usage: 20.1+ KB\n"
     ]
    }
   ],
   "source": [
    "jump_threshold=zgpa_df.close.median()*0.0095\n",
    "print(jump_threshold)\n",
    "zgpa_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "46ae56b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>jump</th>\n",
       "      <th>jump_power</th>\n",
       "      <th>close</th>\n",
       "      <th>date</th>\n",
       "      <th>netChangeRatio</th>\n",
       "      <th>pre_close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-02-24</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.308379</td>\n",
       "      <td>51.460000</td>\n",
       "      <td>2022-02-24</td>\n",
       "      <td>-0.027037</td>\n",
       "      <td>52.890000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-22</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.144831</td>\n",
       "      <td>53.070000</td>\n",
       "      <td>2022-02-22</td>\n",
       "      <td>-0.020126</td>\n",
       "      <td>54.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-07</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.492369</td>\n",
       "      <td>51.980000</td>\n",
       "      <td>2022-02-07</td>\n",
       "      <td>0.040224</td>\n",
       "      <td>49.970000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-08</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.577598</td>\n",
       "      <td>52.100000</td>\n",
       "      <td>2021-10-08</td>\n",
       "      <td>0.077337</td>\n",
       "      <td>48.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-22</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.226605</td>\n",
       "      <td>47.780000</td>\n",
       "      <td>2021-09-22</td>\n",
       "      <td>-0.018488</td>\n",
       "      <td>48.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-27</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.512813</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>2021-08-27</td>\n",
       "      <td>0.033797</td>\n",
       "      <td>50.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-26</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.124388</td>\n",
       "      <td>54.500000</td>\n",
       "      <td>2021-07-26</td>\n",
       "      <td>-0.054476</td>\n",
       "      <td>57.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-02</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.410596</td>\n",
       "      <td>62.300000</td>\n",
       "      <td>2021-07-02</td>\n",
       "      <td>-0.037986</td>\n",
       "      <td>64.760000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                jump  jump_power     close       date  netChangeRatio  \\\n",
       "2022-02-24 -1.000000    1.308379 51.460000 2022-02-24       -0.027037   \n",
       "2022-02-22 -1.000000    1.144831 53.070000 2022-02-22       -0.020126   \n",
       "2022-02-07  1.000000    1.492369 51.980000 2022-02-07        0.040224   \n",
       "2021-10-08  1.000000    3.577598 52.100000 2021-10-08        0.077337   \n",
       "2021-09-22 -1.000000    1.226605 47.780000 2021-09-22       -0.018488   \n",
       "2021-08-27  1.000000    1.512813 52.000000 2021-08-27        0.033797   \n",
       "2021-07-26 -1.000000    1.124388 54.500000 2021-07-26       -0.054476   \n",
       "2021-07-02 -1.000000    1.410596 62.300000 2021-07-02       -0.037986   \n",
       "\n",
       "            pre_close  \n",
       "2022-02-24  52.890000  \n",
       "2022-02-22  54.160000  \n",
       "2022-02-07  49.970000  \n",
       "2021-10-08  48.360000  \n",
       "2021-09-22  48.680000  \n",
       "2021-08-27  50.300000  \n",
       "2021-07-26  57.640000  \n",
       "2021-07-02  64.760000  "
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jump_pd=pd.DataFrame()\n",
    "def judge_jump(today):\n",
    "    global jump_pd\n",
    "    if today.netChangeRatio>0 and (today.low-today.pre_close)>jump_threshold:\n",
    "        \"\"\"\n",
    "        符合向上跳空\n",
    "        \"\"\"\n",
    "        today['jump']=1\n",
    "        today['jump_power']=(today.low-today.pre_close)/jump_threshold\n",
    "        jump_pd=jump_pd.append(today)\n",
    "    elif today.netChangeRatio<0 and (today.pre_close-today.high)>jump_threshold:\n",
    "        \"\"\"\n",
    "        符合向下跳空\n",
    "        \"\"\"\n",
    "        today['jump']=-1\n",
    "        today['jump_power']=(today.pre_close-today.high)/jump_threshold\n",
    "        jump_pd=jump_pd.append(today)\n",
    "for kl_index in np.arange(0, zgpa_df.shape[0]):\n",
    "    today=zgpa_df.iloc[kl_index]\n",
    "    #print(today)\n",
    "    judge_jump(today)\n",
    "jump_pd.filter(['jump','jump_power','close','date', 'netChangeRatio','pre_close'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "b5e0c82d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2022-03-01    20220301\n",
       "2022-02-28    20220228\n",
       "2022-02-25    20220225\n",
       "2022-02-24    20220224\n",
       "2022-02-23    20220223\n",
       "                ...   \n",
       "2021-07-07    20210707\n",
       "2021-07-06    20210706\n",
       "2021-07-05    20210705\n",
       "2021-07-02    20210702\n",
       "2021-07-01    20210701\n",
       "Name: trade_date, Length: 161, dtype: int64"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from canna import ABuMarketDrawing\n",
    "zgpa_df.set_index('date',append=True)\n",
    "jump_pd.index\n",
    "\n",
    "pd.to_numeric( zgpa_df['trade_date'], errors='coerce').fillna('0').astype('int64')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "317f3be7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 161 entries, 2022-03-01 to 2021-07-01\n",
      "Data columns (total 16 columns):\n",
      " #   Column          Non-Null Count  Dtype         \n",
      "---  ------          --------------  -----         \n",
      " 0   ts_code         161 non-null    object        \n",
      " 1   trade_date      161 non-null    int64         \n",
      " 2   open            161 non-null    float64       \n",
      " 3   high            161 non-null    float64       \n",
      " 4   low             161 non-null    float64       \n",
      " 5   close           161 non-null    float64       \n",
      " 6   pre_close       161 non-null    float64       \n",
      " 7   change          161 non-null    float64       \n",
      " 8   pct_chg         161 non-null    float64       \n",
      " 9   vol             161 non-null    float64       \n",
      " 10  amount          161 non-null    float64       \n",
      " 11  netChangeRatio  161 non-null    float64       \n",
      " 12  positive        161 non-null    int64         \n",
      " 13  date            161 non-null    datetime64[ns]\n",
      " 14  date_week       161 non-null    int64         \n",
      " 15  volume          161 non-null    int64         \n",
      "dtypes: datetime64[ns](1), float64(10), int64(4), object(1)\n",
      "memory usage: 21.4+ KB\n"
     ]
    }
   ],
   "source": [
    "zgpa_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "647c4007",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>netChangeRatio</th>\n",
       "      <th>positive</th>\n",
       "      <th>date</th>\n",
       "      <th>date_week</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-03-01</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20220301</td>\n",
       "      <td>51.010000</td>\n",
       "      <td>51.400000</td>\n",
       "      <td>50.570000</td>\n",
       "      <td>51.390000</td>\n",
       "      <td>50.760000</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>1.241100</td>\n",
       "      <td>568035.790000</td>\n",
       "      <td>2895181.121000</td>\n",
       "      <td>0.012411</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>1</td>\n",
       "      <td>568035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-28</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20220228</td>\n",
       "      <td>51.100000</td>\n",
       "      <td>51.100000</td>\n",
       "      <td>50.250000</td>\n",
       "      <td>50.760000</td>\n",
       "      <td>51.400000</td>\n",
       "      <td>-0.640000</td>\n",
       "      <td>-1.245100</td>\n",
       "      <td>731883.260000</td>\n",
       "      <td>3707888.597000</td>\n",
       "      <td>-0.012451</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-02-28</td>\n",
       "      <td>0</td>\n",
       "      <td>731883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-25</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20220225</td>\n",
       "      <td>51.700000</td>\n",
       "      <td>52.060000</td>\n",
       "      <td>51.210000</td>\n",
       "      <td>51.400000</td>\n",
       "      <td>51.460000</td>\n",
       "      <td>-0.060000</td>\n",
       "      <td>-0.116600</td>\n",
       "      <td>544416.390000</td>\n",
       "      <td>2808109.116000</td>\n",
       "      <td>-0.001166</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-02-25</td>\n",
       "      <td>4</td>\n",
       "      <td>544416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-24</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20220224</td>\n",
       "      <td>52.150000</td>\n",
       "      <td>52.250000</td>\n",
       "      <td>51.090000</td>\n",
       "      <td>51.460000</td>\n",
       "      <td>52.890000</td>\n",
       "      <td>-1.430000</td>\n",
       "      <td>-2.703700</td>\n",
       "      <td>975396.020000</td>\n",
       "      <td>5039207.433000</td>\n",
       "      <td>-0.027037</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-02-24</td>\n",
       "      <td>3</td>\n",
       "      <td>975396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-23</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20220223</td>\n",
       "      <td>53.100000</td>\n",
       "      <td>53.300000</td>\n",
       "      <td>52.690000</td>\n",
       "      <td>52.890000</td>\n",
       "      <td>53.070000</td>\n",
       "      <td>-0.180000</td>\n",
       "      <td>-0.339200</td>\n",
       "      <td>463601.660000</td>\n",
       "      <td>2449763.789000</td>\n",
       "      <td>-0.003392</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-02-23</td>\n",
       "      <td>2</td>\n",
       "      <td>463601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-07</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20210707</td>\n",
       "      <td>62.690000</td>\n",
       "      <td>63.410000</td>\n",
       "      <td>62.090000</td>\n",
       "      <td>62.370000</td>\n",
       "      <td>62.900000</td>\n",
       "      <td>-0.530000</td>\n",
       "      <td>-0.842600</td>\n",
       "      <td>523167.540000</td>\n",
       "      <td>3272128.188000</td>\n",
       "      <td>-0.008426</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-07-07</td>\n",
       "      <td>2</td>\n",
       "      <td>523167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-06</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20210706</td>\n",
       "      <td>62.420000</td>\n",
       "      <td>63.140000</td>\n",
       "      <td>61.850000</td>\n",
       "      <td>62.900000</td>\n",
       "      <td>61.910000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>1.599100</td>\n",
       "      <td>685812.850000</td>\n",
       "      <td>4296674.949000</td>\n",
       "      <td>0.015991</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-07-06</td>\n",
       "      <td>1</td>\n",
       "      <td>685812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-05</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20210705</td>\n",
       "      <td>61.990000</td>\n",
       "      <td>62.280000</td>\n",
       "      <td>61.130000</td>\n",
       "      <td>61.910000</td>\n",
       "      <td>62.300000</td>\n",
       "      <td>-0.390000</td>\n",
       "      <td>-0.626000</td>\n",
       "      <td>750646.750000</td>\n",
       "      <td>4629283.875000</td>\n",
       "      <td>-0.006260</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-07-05</td>\n",
       "      <td>0</td>\n",
       "      <td>750646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-02</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20210702</td>\n",
       "      <td>64.030000</td>\n",
       "      <td>64.070000</td>\n",
       "      <td>62.100000</td>\n",
       "      <td>62.300000</td>\n",
       "      <td>64.760000</td>\n",
       "      <td>-2.460000</td>\n",
       "      <td>-3.798600</td>\n",
       "      <td>1028081.620000</td>\n",
       "      <td>6464701.802000</td>\n",
       "      <td>-0.037986</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-07-02</td>\n",
       "      <td>4</td>\n",
       "      <td>1028081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-01</th>\n",
       "      <td>601318.SH</td>\n",
       "      <td>20210701</td>\n",
       "      <td>64.260000</td>\n",
       "      <td>65.170000</td>\n",
       "      <td>63.580000</td>\n",
       "      <td>64.760000</td>\n",
       "      <td>64.280000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.746700</td>\n",
       "      <td>692617.350000</td>\n",
       "      <td>4449223.158000</td>\n",
       "      <td>0.007467</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>3</td>\n",
       "      <td>692617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>161 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              ts_code  trade_date      open      high       low     close  \\\n",
       "date                                                                        \n",
       "2022-03-01  601318.SH    20220301 51.010000 51.400000 50.570000 51.390000   \n",
       "2022-02-28  601318.SH    20220228 51.100000 51.100000 50.250000 50.760000   \n",
       "2022-02-25  601318.SH    20220225 51.700000 52.060000 51.210000 51.400000   \n",
       "2022-02-24  601318.SH    20220224 52.150000 52.250000 51.090000 51.460000   \n",
       "2022-02-23  601318.SH    20220223 53.100000 53.300000 52.690000 52.890000   \n",
       "...               ...         ...       ...       ...       ...       ...   \n",
       "2021-07-07  601318.SH    20210707 62.690000 63.410000 62.090000 62.370000   \n",
       "2021-07-06  601318.SH    20210706 62.420000 63.140000 61.850000 62.900000   \n",
       "2021-07-05  601318.SH    20210705 61.990000 62.280000 61.130000 61.910000   \n",
       "2021-07-02  601318.SH    20210702 64.030000 64.070000 62.100000 62.300000   \n",
       "2021-07-01  601318.SH    20210701 64.260000 65.170000 63.580000 64.760000   \n",
       "\n",
       "            pre_close    change   pct_chg            vol         amount  \\\n",
       "date                                                                      \n",
       "2022-03-01  50.760000  0.630000  1.241100  568035.790000 2895181.121000   \n",
       "2022-02-28  51.400000 -0.640000 -1.245100  731883.260000 3707888.597000   \n",
       "2022-02-25  51.460000 -0.060000 -0.116600  544416.390000 2808109.116000   \n",
       "2022-02-24  52.890000 -1.430000 -2.703700  975396.020000 5039207.433000   \n",
       "2022-02-23  53.070000 -0.180000 -0.339200  463601.660000 2449763.789000   \n",
       "...               ...       ...       ...            ...            ...   \n",
       "2021-07-07  62.900000 -0.530000 -0.842600  523167.540000 3272128.188000   \n",
       "2021-07-06  61.910000  0.990000  1.599100  685812.850000 4296674.949000   \n",
       "2021-07-05  62.300000 -0.390000 -0.626000  750646.750000 4629283.875000   \n",
       "2021-07-02  64.760000 -2.460000 -3.798600 1028081.620000 6464701.802000   \n",
       "2021-07-01  64.280000  0.480000  0.746700  692617.350000 4449223.158000   \n",
       "\n",
       "            netChangeRatio  positive       date  date_week   volume  \n",
       "date                                                                 \n",
       "2022-03-01        0.012411         1 2022-03-01          1   568035  \n",
       "2022-02-28       -0.012451         0 2022-02-28          0   731883  \n",
       "2022-02-25       -0.001166         0 2022-02-25          4   544416  \n",
       "2022-02-24       -0.027037         0 2022-02-24          3   975396  \n",
       "2022-02-23       -0.003392         0 2022-02-23          2   463601  \n",
       "...                    ...       ...        ...        ...      ...  \n",
       "2021-07-07       -0.008426         0 2021-07-07          2   523167  \n",
       "2021-07-06        0.015991         1 2021-07-06          1   685812  \n",
       "2021-07-05       -0.006260         0 2021-07-05          0   750646  \n",
       "2021-07-02       -0.037986         0 2021-07-02          4  1028081  \n",
       "2021-07-01        0.007467         1 2021-07-01          3   692617  \n",
       "\n",
       "[161 rows x 16 columns]"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d=pd.to_numeric( zgpa_df['trade_date'], errors='coerce').fillna('0').astype('int64')\n",
    "d['date']=zgpa_df['date']\n",
    "\n",
    "\n",
    "zgpa_df.index=zgpa_df['date']\n",
    "zgpa_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "fdae8882",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#zgpa_df[['close']].values\n",
    "zgpa_df.index\n",
    "zgpa_df['volume']=zgpa_df['vol'].copy()\n",
    "zgpa_df.index.name=None\n",
    "zgpa_df['volume']=zgpa_df['volume'].astype(np.int64)\n",
    "zgpa_df.head()\n",
    "# Volume数值类型对应的应该是int64,不是float64\n",
    "ABuMarketDrawing.plot_candle_form_klpd(zgpa_df, view_indexs=jump_pd.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "607a9553",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
